The Coherent Ising Machine Shows the Most Promise in Quest for Quantum Computing

Classical Computing
As compared to classical computers, the CIM has two distinct advantages: speed and energy efficiency. The use of optics and lasers in the CIM give it a big advantage in terms of bandwidth – about 200THz, vs. a few GHz for classical – which leads to faster communications and computations. Optical circuits, as opposed to electrical circuits, can also perform simultaneous computations while minimizing energy consumption.

Quantum Approximate Optimization Algorithm and Quantum Gates
The quantum approximate optimization algorithm (QAOA) is a quantum gate approach that is highly efficient but not definitive in terms of superiority over classical computing. The algorithm has been touted as an effective solution for solving combinatorial optimization problems on gate-based quantum computers. But there is little evidence this is actually faster than running an optimization problem on a commercially available computer.

Quantum gated computing uses commercially available hardware to perform calculations. Although it can be successfully implemented, the system has significant limitations with scalability as compared to the CIM. When running algorithms on superconducting hardware, to ensure reliable, low-noise qubit operation the hardware must be reduced to a temperature of about 5 millikelvin (mK). This is challenging because it requires dilution refrigerators and the chips are highly sensitive to even small amounts of heating, making scaling to many qubits difficult.

Quantum gate computing with error correction can theoretically be exponentially faster than a classical computer at solving certain problems like factoring, simulating complex molecules, or complex quantum dynamics. But there are few known cases at present where a quantum computer is known to run much faster than a standard laptop.

Quantum Annealing
Quantum annealing and the CIM are both spin systems that contain qubits in superpositions, allowing for an optimization function to be encoded. But the systems tackle this using different approaches.

With quantum annealing, if there is no noise within the system and the anneal is performed sufficiently slowly, it stays in the ground state. But for many problems, the required anneal time is exponentially long, making it an impractical solution.

The CIM starts in one state that’s a mix of all the desired states. Beginning in a vacuum state with OPOs below threshold, the CIM gradually increases the pump power until the OPOs are above the threshold. From here, the vacuum state settles into a local minimum and produces the solution. Due to its optical nature, the CIM is not subject to thermal noise.

Digital Annealers
Digital annealers are a type of classical computer that solve combinatorial optimization problems at high speed with digital circuits inspired by quantum phenomena. Their design solves one specific problem with one algorithm, typically an Ising problem. While digital annealers can be much faster than CPUs/GPUs because their circuitry is optimized, they are still bottlenecked by digital CMOS hardware constraints. So, other platforms that aren’t subject to those constraints, including quantum annealers and CIMs, may still outperform digital annealers.

Use Cases and Applications for the CIM

With its ability to solve difficult problems that would take years for a traditional computer, the CIM may prove useful for a number of applications.

Drug Discovery
Testing drug candidates through trial and error is a costly and inefficient process for pharmaceutical companies. Chemicals can be arranged in an exponential number of combinations, but only a handful of them result in compounds that produce successful results in trials. The CIM could potentially allow researchers to test different combinations and identify promising ones much faster, saving money, resources and time.

Supply Chain and Logistics
Supply chain and logistics applications must identify the most efficient way to get goods from point A to point B. But with numerous different potential routes, it represents a classic complex problem, especially as the number of possible destinations grows. A classic example is the traveling salesman problem – a salesman who needs to find the most efficient route to travel among numerous cities. Even with just 5 cities, the salesman has 120 possible routes. With 32 cities, the number of combinations increases to 2.63 x 1035 ¬– beyond the scope of classical computers to solve efficiently.

Financial Applications
Similar to the traveling salesman problem, financial applications involve numerous potential factors and variables, including fixed amounts of money in a portfolio or pools of portfolios, risk and legal factors, varying interest rates and more. These numerous factors make it difficult to find optimal solutions on a traditional computer. The CIM could take numerous factors into account and produce optimized solutions.

Artificial Intelligence and Machine Learning
The CIM may also prove to be a beneficial tool for artificial intelligence applications such as machine learning. Research is currently underway at MIT on applying CIM-like hardware to accelerate deep neural networks. These experimental implementations of the CIM take advantage of fiber-optic time multiplexing, which enables optical data to be multiplexed both in time and space on different wavelengths. This enables hardware to make maximum use of energy, producing high overall compute performance with great efficiency.

CIM Challenges and Solutions

Despite the potential of the CIM, a number of challenges need to be addressed, many having to do with OPOs. This include:

• Driving OPOs to operate close to their maximum threshold by limiting natural quantum noise. Work is underway on creating higher levels of squeezed light as one potential solution to dealing with the issue.
• Creating more flexible and adaptable CIM hardware, especially to different kinds of connectivity between spins. Today, connectivity is largely limited to nearest neighbor spins. Integrating field programmable gate arrays (FPGAs) is one potential solution because of their intrinsic parallelism and flexible architecture.
• When a given Ising problem is divided into two branches, the amplitude of the OPOs is not the same. But in order to get a system that satisfies the Ising model’s restraints, all OPOs need the same amplitude. Through real time error correction feedback, amplitude control forces the correct constraints within the Ising model, leading to a more accurate solution.

The Future of the CIM

Since 2019, NTT Research has committed to numerous joint CIM research projects with academic institutions, government agencies and software companies, including:

• The University of Notre Dame researches the limits of continuous-time analog computing in order to explore avenues for improving CIM performance.
• Tokyo Institute of Technology focuses on developing applications for the CIM in compressed sending and drug discovery.
• California Institute of Technology (Caltech) develops a high-speed, miniature CIM consisting of an on-chip 100 GHz pulsed pump laser source and on-chip parametric oscillator device.
• Cornell University explores quantum neural networks by developing error detection and error correction feedback.
• Massachusetts Institute of Technology (MIT) works on optical neural network hardware for accelerating matrix multiplication.
• Stanford University investigates novel optical and superconducting devices for studying quantum-to-classical crossover physics and critical phenomena in the quantum neural network.
• Swinburne University of Technology sets out to develop and implement theoretical models for the CIM.
• The University of Michigan performs theoretical studies of topological states in nonlinear optics and synthetic topological matter.
• NASA Ames Research Center conducts benchmark studies of CIMs compared to modern heuristics on various optimization problems.

Some researchers predict a commercially available CIM by the early 2030s. If true, the computer would likely appear similar to LASOLV, a computing machine developed by NTT based on photonics technologies. LASOLV is currently operating under the consulting of NTT Computer and Data Science Laboratories. You can read more about LASOLV on the NTT website.

The concepts behind the CIM have been demonstrated, and through ongoing collaboration researchers continue to push the frontiers of performance.