Tag: quantum computing

The U.S. Department of Energy (DOE) announced today the creation of several multidisciplinary Quantum Information Science Research Centers in support of the National Quantum Initiative.

Today marks one of the U.S. government’s largest investments in this field. It is also a noteworthy moment for Microsoft, which is providing scientific leadership, in addition to expertise in workforce development and technology transfer.

Microsoft is one of the five core founding members of one of the newly-formed centers, the Quantum Science Center (QSC), along with Oak Ridge National Laboratory, Los Alamos National Laboratory, Fermi National Accelerator Laboratory, and Purdue University. In addition to the Quantum Science Center, Microsoft is also a partner in the Q-NEXT center, led by Argonne National Laboratory and joined by Stanford Linear Accelerator Center. And finally, Microsoft is enrolled in the External Advisory Board of the Quantum Science Accelerator Center, led by Lawrence Berkeley National Laboratory and joined by Sandia National Laboratory.

Pacific Northwest National Laboratory (PNNL), a longstanding DOE Lab collaborator with Microsoft and co-founding member of Northwest Quantum Nexus, will also participate in QSC and Q-NEXT. In both these Centers, Microsoft and PNNL will continue their work on quantum chemistry, algorithms, and tools, leveraging earlier innovations involving NWChem and the QDK.

As has been our impact and experience with other collaborations involving U.S. government entities, universities, and Microsoft, the newly-created QIS Research centers will bring together the best of the public and private sector together to solve the scientific problems that lie on the path to a commercial-scale quantum computer.

While quantum computing will someday have a profound impact, today’s quantum computing systems are still nascent technologies. To scale these systems, we must overcome a number of scientific challenges.

Microsoft has been tackling these challenges head-on through our work towards developing topological qubits, classical information processing devices for quantum control, new quantum algorithms, and simulations. Our team has been collaborating with universities globally since its inception, even opening labs on the campuses of UC Santa Barbara, Purdue University, the University of Sydney, Copenhagen University, and the Technical University of Delft. With today’s announcement, the efforts and expertise of this global network will be taken to the next level across a number of areas.

We believe that we will need to explore new materials combinations in order to realize significant performance improvements in topological qubits, and DOE National Labs have vast experience with the exploration of materials. They also have unique tools, such as a spallation neutron source and synchrotrons for probing the properties of these materials in order to screen them for use in quantum devices. Together, Microsoft and its partners in the DOE’s labs can design the probes of the future that are tailored for topological quantum materials.

At the other end of the quantum computing stack, the Centers can bolster our efforts to benchmark quantum algorithms and protocols for qubit validation and verification. Oak Ridge’s Leadership Computing Facility features a number of near-term quantum computing testbeds, while our Azure Quantum service is backed by Microsoft’s cloud computing expertise and infrastructure, as well as decades of research in quantum algorithms and languages such as our high-level quantum programming language Q#, which can be targeted at near-term quantum computing testbeds and also at the quantum computers of the future.

Today’s announcement connects extensive public-private expertise, resources, and funding to tackle the tough problems ahead and accelerate progress.

Even with the recent decrease in cars on the road, we’ve all had the experience of being stopped at a traffic signal, waiting for the light to turn green, only to be stopped by another red light one block later. Besides being a frustrating driving experience, this inefficient idling time contributes considerably to carbon emissions. If we can optimize the timing of traffic signals to reduce this waiting time, we could improve the flow of traffic and ultimately lessen our impact on the environment.

The challenge with optimization problems like this one is that when the number of variables increases (e.g., volume of vehicles, number of signals, time of day), the computational requirements to calculate the best solution (e.g., efficient signaling time) grows quickly with today’s classical computers.

In nature, we find efficient solutions to complex optimization problems that can be a great source of inspiration when designing new algorithms. Nature speaks the language of quantum mechanics and emulating these unique quantum properties can lead to powerful new optimization techniques. For example, by emulating quantum tunneling on classical hardware we can efficiently find solutions to instances of hard combinatorial problems. Similarly, by using tempering techniques like those used in metallurgy when hardening steel, we can efficiently solve hard optimization problems.

In Azure Quantum, we have developed optimization techniques inspired by natural processes for binary optimization problems. This approach allows for the native encoding of higher-order interactions on an all-to-all graph, meaning that no mapping or embedding is needed, ultimately unlocking applications that are seemingly intractable.

Jij Inc. and Toyota Tsusho are working together to begin tackling mobility and traffic challenges using quantum-inspired optimization (QIO) in Azure Quantum. Founded in 2018 by theoretical physicists, Japanese startup Jij helps businesses like Toyota Tsusho experiment with new computing techniques and apply quantum research to solve real-world problems.

Traditional methods for traffic signal optimization treat each vehicle independently in large-scale simulations that are computationally expensive and slow. Those methods are unable to factor in higher-cost variables, such as the correlation of traffic flow between signals.

To help Toyota Tsusho find a better solution, Jij proposed a Polynomial Unconstrained Binary Optimization (PUBO) formulation, requiring higher-order terms. Solving this PUBO representation of the problem using QIO in Azure Quantum, Jij and Toyota Tsusho were able to reduce car waiting times by 20% when compared to traditional methods with large-scale simulation.

We previously had to simulate traffic for each individual light to find an improved sequence, but that approach was limited because we couldn’t factor in the time correlation of traffic flow between lights. Now with Azure Quantum, we can address this problem from a more systems-level approach. Collaborating with Toyota Tsusho, we seek to improve the timing of large-scale traffic networks, resulting in potential economic and environmental benefits for many cities.

– Kohji Nishimura, CTO, Jij

By building on the same foundational principles as nature, we are moving towards a vision of optimizing entire holistic environments in a way that is not possible with today’s classical systems—fundamentally changing the way people, goods, and services move through cities, countries, and around the world.

Microsoft is partnering with Microsoft Quantum Network members like Jij to realize this vision by supporting their development of practical solutions and accelerating customer impact through Azure Quantum.

Jij and their early work on traffic signal optimization with Toyota Tsusho is a first step toward preparing for a world where scaled quantum computers are more readily available.

We are looking forward to seeing the progress toward a changing transportation landscape—where economic and environmental benefits are increased in cities around the world, ultimately improving the quality of life for all.

For a third-party perspective on our work with Jij and Toyota Tsusho, check out this recent article from The Wall Street Journal. If you would like to learn more about quantum-inspired optimization or ways you can get involved with Microsoft Quantum and Azure Quantum, please reference the links below.

Apply to become an early adopter of Azure Quantum

Request to join the Microsoft Quantum Network

Learn more about quantum-inspired optimization with Microsoft Learn

The promise of quantum computing is that it could help us solve some of the most complex challenges facing our world. It’s commonly told that addressing global issues, like climate change, would take our classical computers of today billions of years to solve, whereas a quantum computer could find solutions in mere weeks, days, or hours.

This speedup over classical computing comes when researchers develop algorithms that can harness the unique principles of quantum mechanics—superposition and entanglement—to process highly complex calculations more quickly on scaled-up quantum hardware.

To prepare for this scaled-up quantum future, researchers have been discovering which problems will be well suited for quantum computing and how big of a speedup we can expect over classical counterparts—whether polynomial or exponential.

Shor’s algorithm, for example, is a famous quantum algorithm that we know will yield exponential speedups on a scaled quantum computer. This understanding from research has already had a significant impact on the security industry, reshaping the way we encrypt and protect our data for years to come.

But for other types of common problems, the maximum impact quantum can have has remained an open question for decades—until now.

Robin Kothari, a Senior Researcher on the Microsoft Quantum Systems team, and fellow collaborators  Scott Aaronson, Shalev Ben-David, and Avishay Tal, have discovered a breakthrough in two common problems that have been open for over twenty years, resolving conjecture about the speedup that is obtainable by quantum algorithms over classical algorithms.

The problems the team explored appear in almost every type of industry—analyzing massive sets of unstructured data and understanding the connections and patterns within a large graph or network. The researchers showed that the best possible speedup is quartic, meaning to the fourth power, for unstructured problems in quantum query complexity. Previously, in 1998 it was only proven that, at most, a sixth power speedup was the best possible. The proof technique they used also resolves a question having to do with quantum speedup for graph problems.

By definitively answering the question of the largest possible quantum speedup for these problems, the team has enabled fellow researchers and organizations across the industry to better understand both the opportunities and limits of these algorithms and to continue focusing on problems that hold promise for future quantum impact.

You can learn more about the research in a Microsoft Research Blog post by Robin Kothari and read Quantum Implications of Huang’s Sensitivity Theorem published on arXiv.

The promise of quantum computing is that it will help us solve some of the world’s most complex challenges. When designed to scale, quantum systems will have capabilities that exceed our most powerful supercomputers. We’re seeing this begin to take shape even today, with early breakthroughs in material design, financial risk management, and MRI technology. As the global community of quantum researchers, scientists, engineers, and business leaders continue to collaborate to advance the quantum ecosystem, we expect to see quantum impact accelerate across every industry.

However, this same computing power that will unlock solutions to complex challenges will also break some of today’s most sophisticated cryptography. By anticipating the technology of the future, Microsoft Research – in collaboration with academic and industry partners – is getting ready to accept the challenge it poses by preparing customers for a post-quantum world, today.

Cryptography today

Cryptography – the science of encrypting and decrypting data – ensures the confidentiality of the private communications of individuals and organizations online.  Encryption is used to protect everything from sending text messages to your friends, to banks transferring billions of dollars to other banks, and these transactions happen in a matter of milliseconds. Online encryption scenarios typically use a combination of two techniques: symmetric-key cryptography and public-key cryptography. In symmetric-key cryptography, the sender and the recipient must know (and keep secret from everyone else) a shared encryption key that is used to encrypt and decrypt the messages to be sent. Public-key cryptography, in contrast, allows two parties to send and receive encrypted messages without any prior sharing of keys. It was the discovery of public-key cryptosystems (by Merkel, Diffie, and Hellman in 1976 and Rivest, Shamir, and Adelman in 1978) that allows us to connect securely with anyone in the world, whether we’ve exchanged data before or not, and to do it so fast that we don’t even realize it’s happening.

Classical vs. quantum computing

The public-key cryptosystems that we use today are based on certain hard mathematical problems. For example, the security of the RSA public-key cryptosystem rests on the difficulty of factoring products of two large prime numbers – if we take two 300-digit prime numbers we can easily multiply them together to get a ~600-digit product, but if we start with just the product it is difficult to figure out the two smaller factors, no matter how much classical computing power is available for the task.

In the early ’90s, Dr. Peter Shor at AT&T Bell Laboratories discovered an algorithm that could factor products of two large prime numbers quickly, but his algorithm requires a quantum computer in order to run. Now known as “Shor’s Algorithm,” his technique defeats the RSA encryption algorithm with the aid of a “big enough” quantum computer. A quantum computer with enough stable qubits to use Shor’s Algorithm to break today’s public-key cryptography is fairly far out, but the risk is on the horizon. Further, an adversary could be recording encrypted internet traffic now for decryption later, when a sufficiently large quantum computer becomes available. In this way, future quantum computers are a threat to the long-term security of today’s information.

Post-quantum cryptography

To address this threat, the US National Institute of Standards and Technology (NIST) – whose charter is to promote innovation and industrial competitiveness across a broad spectrum of technologies and endeavors, including cybersecurity – has begun the process of standardizing new public-key cryptographic algorithms that cannot be attacked efficiently even with the aid of quantum computer. With participants from around the globe, this project’s goal is to identify new cryptographic algorithms that are resistant to attacks by quantum computers and then standardize them for broad use.

NIST’s initial call for proposals attracted sixty-nine total submissions from around the world for key exchange and digital signature algorithms, including four proposals co-submitted by Microsoft Research. In January 2019, NIST selected twenty-six of those proposals to move forward to Round 2 of the selection process, including all four of the Microsoft Research co-submissions. Here’s a list of the proposals in which Microsoft Research is a partner:

• Key encapsulation mechanisms (KEMs):
• Digital signature schemes:
  • Picnic: A digital signature scheme based on zero-knowledge proofs of knowledge and multi-party computation.
  • qTESLA: A lattice-based signature scheme.

How do we protect our customers?

It will be several more years before NIST finishes its process of selecting and standardizing new post-quantum algorithms. In the meantime, we need to get to work today to begin protecting our customers and their data from future attacks. We know it will take time to migrate all of today’s existing services and applications to new post-quantum public-key algorithms – replacing cryptographic algorithms in widely deployed systems can take years and we need a solution that can provide protection while that work is ongoing.

One approach Microsoft Research is exploring is applying the new post-quantum cryptography to network tunnels. By using both current algorithms and post-quantum algorithms simultaneously – what we call a “hybrid” approach – we comply with regulatory requirements such as FIPS (Federal Information Processing Standards) while protecting against both today’s classical attackers and tomorrow’s quantum-enabled ones.

To test this technology, Microsoft is turning to Project Natick, a years-long research effort to investigate manufacturing and operating environmentally-sustainable, prepackaged datacenter units that can be ordered to size, rapidly deployed and left to operate, lights out, on the seafloor for years. While tunneling can certainly be tested in dry environments, by putting this technology to the test under more difficult circumstances (underwater), on non-production data (safe to test), we have a good representation of what an actual data center customer experience would look like, under stress.

As Karen Easterbrook, Senior Principal PM Manager at Microsoft Research says, “If we can get this to work underwater, then we can get this to work anywhere… We want post-quantum cryptography to be running on every link between every Microsoft datacenter and ultimately between every Microsoft datacenter and every Microsoft customer. And this is a necessary first step toward being able to make that happen.”

Getting ready for a post-quantum world

Dr. Brian LaMacchia, Distinguished Engineer and Head of the Security and Cryptography Group at Microsoft Research, says, “The best way to start preparing is to ensure that all current and future systems have cryptographic agility – the ability to be easily reconfigured to add quantum-resistant algorithms.”

By working in partnership with collaborators around the world to develop post-quantum cryptographic algorithms and then applying them to common internet security protocols and use cases, we can use the power of quantum computing to tackle the large-scale problems facing our planet while also ensuring that all of our information remains safe and secure.

Learn more about quantum computing, quantum algorithms including Shor’s algorithm, and Microsoft Quantum:

Whether you’ve noticed or not, you probably spend at least some part of your day staring into an OLED (organic LED) display, as they are found in smartphones, tablets, televisions, and computer monitors, to name just a few applications. OLED displays use organic carbon-based molecules to generate light of different colors under an applied electrical current.

Breakthroughs in displays, and most other technological fields, can be traced back to advances in materials science that enable the discovery of advanced materials with unique properties. However, designing new materials with specific desired attributes is extremely difficult because small changes in the structure of atoms that make up a material can dramatically influence its properties.

Computational chemistry simulations can help accelerate the design of new materials, by providing a better understanding of these structure-property relationships. These simulations pose a huge computational challenge because of the complexity of simulating the characteristics of quantum physics, which governs the interactions between atoms, but we now have the compute power to solve some problems that previously seemed intractable on classical hardware, leading to breakthroughs in new materials discovery.

OTI Lumionics has developed a fast materials design approach, tailored to OLEDs and other electronic materials, that consists of a combination of machine learning techniques, computational chemistry simulations, optimization, rapid synthesis, and closed-loop feedback from testing of new materials in pilot production. They work with the largest electronics companies in the world to design new materials that are mass-production ready, enabling the next generation of exciting consumer electronics.

One application of OTI Lumionics materials, that has been designed using this approach, is in transparent displays, which will soon be available in smartphones, helping to hide the array of sensors and front-facing camera under the display. When you are “heads-up driving” – viewing your speedometer and mileage on the windshield of your car – you are looking at another application of this technology.

Instead of using a traditional approach to materials discovery which requires synthesizing and testing thousands of variations to find the suitable candidate, OTI Lumionics has developed software tools to simulate and predict the properties of new materials, allowing a larger pool of candidates to be screened than could otherwise be synthesized and tested. Thus, new materials that meet the precise requirements of the largest electronics manufacturers can be “designed” rather than discovered by chance.

The slowest and most expensive part of the workflow is the computational pipeline – the bottleneck on available hardware when running extremely large simulations, which scale exponentially with size. In addition, some simulations are so compute-intensive that they are literally unsolvable with today’s classical computers. The trade-off between simulation accuracy and compute-intensity is thus a major bottleneck in using a computational approach for commercial size problems.

To get around this bottleneck OTI Lumionics has been investigating quantum computing as a potential candidate to help accelerate computational chemistry simulations of new materials. Since many structure-property relationships of materials are governed by quantum physics, quantum computing, which uses quantum mechanical effects to perform computations, is a natural candidate to simulate these systems more accurately.

“Quantum computing has the potential to revolutionize materials design, by enabling highly accurate simulations that could otherwise not be solved on classical hardware. Unfortunately, current gate-based quantum computing is far from being powerful enough to simulate commercial-sized problem,” said OTI Lumionics Head of Materials Discovery, Scott Genin.

Using Azure Quantum and quantum optimization solutions running on classical hardware, Quantum Inspired Optimization (QIO) can enable quantum methods for materials simulations that yield more accurate results.

Scott Genin again: “In the field of computational chemistry, high accuracy property prediction is considered to be very difficult; in fact, some computations are nearly impossible on today’s classical hardware. We have developed new methods, that allow quantum computing algorithms for computational chemistry simulations to be represented as binary optimization problems. Running our quantum computing methods with Azure Quantum optimization solutions, we are getting results that are more accurate than other algorithms.”

As an early adopter of quantum computing, OTI Lumionics has invested in a team of quantum chemists, computer scientists, and software engineers to develop their own quantum computing algorithms and software for materials design, and have made significant theoretical and practical advances in the field. With their algorithms now running on Azure Quantum, OTI Lumionics is able to demonstrate meaningful results on commercially relevant sized problems, today. For example, by using Azure Quantum’s optimization tools in their pipeline, OTI Lumionics successfully performed a complete active space configuration interaction simulation of an archetype green light emitting OLED material – Alq₃ [Tris (8-hydroxyquinolinato) aluminum].

“We have designed our solver platform in Azure Quantum with customers in mind,” said Microsoft Principal Research Manager, Helmut Katzgraber. “Our quantum solutions on classical hardware do not have the limitations of other solvers and optimization hardware and are driven by some of the most powerful algorithms currently available, while being easy to use as there is no need to tune parameters. “

To give you an idea of the computational savings the same simulation of Alq₃ would require 42 error-corrected qubits on gate-based quantum hardware. Mapping the problem to an industry-standard quadratic unconstrained binary optimization (QUBO) using OTI Lumionics reparametrization would require a quantum annealer (or QUBO solver) that could handle 58,265 variables. Solving a QUBO problem with this many variables is intractable, and even an equivalent simulation of Alq₃ using standard classical computational chemistry software would require a supercomputer. In contrast, using Azure Quantum, the higher-order binary problem can be handled natively, meaning that this problem only requires 132 variables on classical hardware to perform the simulation.

“The fact is that we have compelling results that show that we can start using quantum solutions for commercial problems in a matter of months, not years,” said OTI Lumionics Co-founder and CEO, Michael Helander. “Using Azure Quantum, we now have the potential to dramatically increase the accuracy and throughput of the computational chemistry simulations that underpin our entire materials design workflow.”

Using Azure Quantum, OTI Lumionics can open their computational pipeline to run more accurate simulations at significantly higher speeds, which could ultimately lead to timelier and lower cost materials design, and thus better OLED displays.

We are excited to be working with OTI Lumionics in helping them find breakthrough discoveries in materials through quantum computing and Azure Quantum.

Learn more about the Azure Quantum preview and sign up to become an early adopter.

At Microsoft Quantum, our ambition is to help solve some of the world’s most complex challenges through the world’s most scalable quantum system. Recently, we introduced Azure Quantum to unite a diverse and growing quantum community and accelerate the impact of this technology. Whether it’s algorithmic innovation that improves healthcare outcomes or breakthroughs in cryogenic engineering that enable more sustainable systems design, these recent advancements across the stack are bringing the promise of quantum to our world, right now.

In December 2018, the United States Congress signed the National Quantum Initiative Act – an important milestone for investing the resources needed to continue advancing the field. As recognized by the Act, education on quantum information science and engineering needs to be an area of explicit focus, as the shortage of quantum computing talent worldwide poses a significant challenge to accelerating innovation and fully realizing the impact quantum can have on our world.

Leaders across both public and private sectors need to continue working together to develop a global workforce of quantum engineers, researchers, computer and materials scientists, and other industry experts who will be able to carry quantum computing into the future. Microsoft has been collaborating with academic institutions and industrial entities around the world to grow this quantum generation and prepare the workforce for this next technological revolution.

Empowering the quantum generation through education

Earlier this year, Microsoft partnered with the University of Washington to teach an introductory course on quantum computing and programming. The course, led by Dr. Krysta Svore, General Manager of Microsoft Quantum Systems, focused on the practical implementation of quantum algorithms.

Students were first introduced to quantum programming with Q# through a series of coding exercises followed by programming assignments. For their final project, student teams developed quantum solutions for specified problems – everything from entanglement games and key distribution protocols to quantum chemistry and a Bitcoin mining algorithm. Several students from this undergraduate course joined the Microsoft Quantum team for a summer internship, further developing their new skillsets and delivering quantum impact to organizations around the world.

On the heels of this hands-on teaching engagement, Microsoft has established curriculum partnerships with more than 10 institutions around the world to continue closing the skills gap in quantum development and quantum algorithm design. This curriculum is circling the globe, from the University of California, Los Angeles (UCLA) to the Indian Institute of Technology (IIT) in Roorkee and Hyderabad, India.

Partner universities leverage Q#, Microsoft’s quantum programming language and associated Quantum Development Kit, to teach the principles of quantum computing to the next generation of computer engineers and scientists.

“The course material extended to us by Microsoft is concise and challenging. It covers the necessary mathematical foundations of Quantum Computing. Simulation on Q# is quite straightforward and easy to interpret. Collaboration with Microsoft has indeed captivated students of IIT Roorkee to get deeper insights into Quantum Technology.”

– Professor Ajay Wasan of IIT Roorkee, Department of Physics

Q# integrates with familiar tools like Visual Studio and Python, making it a very approachable entry point for undergraduate and graduate students alike.

 “I integrated Microsoft’s Q# into my UCLA graduate course called Quantum Programming.  My students found many aspects of Q# easy to learn and used the language to program and run four quantum algorithms. Thus, the curriculum partnership with Microsoft [has] helped me teach quantum computing to computer science students successfully.”

– Professor Jens Palsberg of UCLA, Computer Science Department

Microsoft has also partnered with Brilliant to bring quantum computing to students and professionals around the world via a self-serve e-learning environment.

This interactive Quantum Computing course introduces students to quantum principles and uses Q# to help people learn to build quantum algorithms, simulating a quantum environment in their browsers. In the last six months, more than 40,000 people have interacted with the course and started building their own quantum solutions.

Accelerating quantum innovation through cross-industry collaboration

Recently, Microsoft enrolled into the Quantum Economic Development Consortium (QED-C), which aims to enable and grow the United States quantum industry.

QED-C was established with support from the National Institute of Standards and Technology (NIST) as part of the federal strategy for advancing quantum information science. Through the QED-C, Microsoft partners with a diverse set of business and academic leaders to identify and address gaps in technology, standards, and workforce readiness facing the quantum industry.

We look forward to continuing our academic and cross-industry collaborations in developing a quantum workforce to tackle real-world scenarios and bring this revolutionary technology to fruition.

Request to be an early adopter of Azure Quantum and incorporate Q# and the QDK in your quantum curriculum.

Are you currently a student interested in joining Microsoft Quantum as an intern? Apply to our open research intern positions today!

Other ways to get involved:

Learning resources:

By Dr. Ken Washington, Chief Technology Officer, Ford Motor Company

Our connected world has helped billions of people improve their lives in numerous ways such as offering instant access to information, enhancing health care, providing new ways to watch movies or experience music, and equipping our homes with smart speakers.

Yet with all these advancements, many of us find ourselves stuck in more traffic, not less. The fantastic navigation technology that anyone can use and helps us more efficiently get places simply does not have the power to coordinate traffic on a mass scale.

But could it? Through a joint research pilot, Ford and Microsoft scientists have simulated thousands of vehicles and their impact on congestion by leveraging powerful quantum-inspired technology. While we’re still in the early stages of quantum computing development, encouraging progress has been made that can help us take what we’ve learned in the field and start to apply it to problems we want to solve today, while scaling to more complex problems tomorrow.

Julie Love, senior director at Microsoft leading their quantum computing business development, says, “Quantum computing has the potential to transform the auto industry and the way we move. To do that we need to have a deep understanding of the problems that companies like Ford want to solve, which is why collaborations like these are so important.”

Our researchers teamed up in 2018 to develop new quantum approaches running on classical computers already available to help reduce Seattle’s traffic congestion.

During rush hour driving, numerous drivers request the shortest possible routes at the same time, but current navigation services handle these requests in a vacuum. They do not take into consideration the number of similar incoming requests, including areas where other drivers are all planning to share the same route segments, when delivering results.

Just imagine a family trying to get ready for work and school in the morning with similar departure times. If an individual day planning app gave each person the quickest way to get going, there likely would be a bottle-neck at the bathroom. Now scale that to a family of thousands…

Instead of this type of individualized routing, what if we could develop a more balanced routing system — one that could consider all the various route requests from drivers and optimize route suggestions so that the number of vehicles sharing the same roads is minimized? That sounds great — and could potentially save everyone time, not to mention aggravation — but one major roadblock towards balanced routing is the fact that it would require extensive computational resources.

Simply put, it’s not feasible to have traditional computers find the optimal solution from a huge number of possible route assignments in a timely manner. That’s where quantum computing can help. Essentially, existing digital computers translate information into either a 1 or a 0, otherwise known as a bit. But in a quantum computer, information can be processed by a quantum bit (or a qubit) that can simultaneously exist in two different states before it gets measured. Upon measurement, however, either a 1 or a 0 appears randomly and the probability for each is governed by a set of rules called quantum mechanics.

This ultimately enables a quantum computer to process information with a faster speed. Attempts to simulate some specific features of a quantum computer on non-quantum hardware have led to quantum-inspired technology — powerful algorithms that mimic certain quantum behaviors and run on specialized conventional hardware. That enables organizations to start realizing some benefits before fully-scaled quantum hardware becomes available.