What can and cannot be done by connecting a quantum computer to a supercomputer

What Becomes Possible and What Remains Impossible by Connecting Quantum Computers and Supercomputers

As highlighted in reports like “A Collaboration Like a Jetliner and a Maglev Train: Growing Expectations for the New Era of IBM Quantum System Two and Japan’s Supercomputer Fugaku”, connecting quantum computers with supercomputers is attracting increasing attention.

Here, we will explore what becomes possible and what remains impossible when quantum computers and supercomputers are connected.

<What Becomes Possible>

1. Hybrid Computing (Leveraging Quantum Advantages)

By combining quantum computers and supercomputers, “hybrid computing” becomes possible, utilizing the respective strengths of both technologies. Supercomputers excel at large-scale parallel processing and classical numerical calculations, making them well-suited for simulations and massive data processing. Quantum computers, on the other hand, demonstrate exponential computational advantages in specific problem areas such as optimization problems, quantum chemistry, prime factorization, and quantum simulations.

By integrating these technologies, hybrid problem-solving approaches are expected to emerge:

• In optimization problems, such as combinatorial optimization and routing, quantum computers can perform high-speed searches for specific subproblems, while supercomputers handle supplementary tasks and overall evaluations.

• In quantum chemistry simulations, quantum computers can tackle electronic structure analysis and other domains that are computationally challenging for classical computers. Supercomputers handle pre-processing, result analysis, and visualization.

• In machine learning, quantum computers can be used for specific sub-processes, such as feature selection or parameter optimization, leading to potential improvements in computational efficiency.

By complementarily combining quantum computers and supercomputers, highly efficient problem-solving becomes achievable in ways that are difficult with either technology alone.

2. Accelerating Pre- and Post-Processing

The collaboration between supercomputers and quantum computers enables role-sharing within computational processes, enhancing overall efficiency and accuracy. In this structure, supercomputers handle massive data organization and pre-processing, quantum computers execute the core calculations, and supercomputers conduct visualization and post-processing of the results.

For example, in quantum chemistry, supercomputers efficiently generate large input parameters (molecular structures, initial conditions, simulation settings). Quantum computers perform core calculations such as quantum mechanical analyses of electronic states, and supercomputers integrate, analyze, and visualize the results for further simulations.

Similarly, in quantum machine learning, supercomputers rapidly design quantum circuits and optimize learning parameters, delegating critical computational components to quantum computers. This allows performance improvements beyond classical methods alone.

Thus, this division of labor maximizes the strengths of both supercomputers and quantum computers, paving a realistic path for practical quantum computing applications.

3. Efficiency Improvements in Quantum Algorithm Development

Current quantum computers face physical limitations and high usage costs, restricting the number of practical operations and runtime. To maximize limited quantum computing resources, thorough pre-simulation and verification are essential.

Supercomputers play a crucial role in simulating quantum circuits and verifying error correction algorithms before deployment. With massive parallel computing capabilities, supercomputers can simulate quantum circuit behavior with high accuracy, enabling optimal circuit design and control strategies to be established before using real quantum hardware.

This approach reduces wasted resources and trial-and-error in the design phase, making efficient development and experimentation of quantum computing systems feasible.

<What Remains Impossible and Limitations>

Despite these advantages, there are significant limitations to the hybrid integration of quantum and supercomputers.

1. Quantum Computers Cannot Fully Replace Classical Systems

Current quantum computers, particularly NISQ (Noisy Intermediate-Scale Quantum) devices, face considerable restrictions in qubit count and error rates. Quantum entanglement and environmental interference degrade computational accuracy, making long-duration, large-scale computations unsuitable.

Supercomputers, with vast parallel processing capabilities and stable operation, remain far superior for massive numerical calculations such as fluid dynamics simulations, weather forecasting, and structural analysis. These areas are far more efficiently handled through classical computing approaches at present.

Thus, quantum computers should be applied selectively to specific problem areas rather than as universal replacements for large-scale numerical calculations.

2. Communication Bottlenecks and Interface Challenges

At present, data transfer between quantum computers and supercomputers remains highly limited. While it is possible to send computational instructions or input data from supercomputers to quantum computers and process quantum output on supercomputers, communication speed and reliability remain significant challenges.

In particular, technologies for high-speed, error-free transfer of quantum states (qubits) over quantum networks, and efficient interfaces connecting quantum and classical systems, are still in early-stage research. Full-scale, real-time, large-scale integration requires further technological advancement.

3. Quantum Computing is Not Universally Advantageous

The computational advantage of quantum computers is limited to specific problem domains. Examples include prime factorization and certain search problems, where quantum algorithms theoretically outperform classical methods significantly.

For instance, Shor’s algorithm allows efficient prime factorization of large integers, a task practically infeasible for classical computers. However, realizing this requires large-scale, highly reliable quantum computers, which have yet to become operational.

Similarly, Grover’s algorithm offers theoretical √N speedup for unstructured database searches but applies only to narrowly defined problem types, not to all computational challenges.

Therefore, quantum computing’s advantage remains problem-dependent, requiring careful assessment of appropriate applications.

Concrete Examples: Latest Research and Project-Based Applications of Quantum × Supercomputer Hybrid Models

Here are some concrete, up-to-date examples of hybrid applications combining quantum computers and supercomputers from cutting-edge research and industry projects:

1. Quantum Chemistry Simulations (New Materials & Drug Discovery)

Example: Joint Research by IBM, BASF, and ExxonMobil

• Supercomputer: Large-scale molecular modeling, chemical reaction condition settings, structural optimization

• Quantum Computer: Electron interactions, energy level calculations (quantum many-body problems)

Details:

• Supercomputers screen vast numbers of molecular candidates

• Quantum computers perform high-precision electronic structure evaluations for promising molecules

• Use Case Examples: Battery material development, catalyst performance prediction

2. Financial Portfolio Optimization (Combinatorial Optimization)

Example: Proof-of-Concept by Fujitsu, Japan Exchange Group (JPX), and D-Wave

• Supercomputer: Large-scale market data analysis, risk assessment, pre-processing

• Quantum Computer: Core combinatorial optimization tasks (investment ratio optimization)

Details:

• In real financial markets, stock selection and risk allocation become extremely high-dimensional optimization problems

• Quantum annealing is used to efficiently explore complex optimization subproblems

• Supercomputers handle pre-processing, post-processing, and simulation of results

3. Logistics & Route Optimization

Example: Volkswagen and D-Wave Traffic Congestion Mitigation Demonstration

• Supercomputer: Real-time analysis of traffic flow data

• Quantum Computer: Combinatorial optimization of shortest path problems and congestion reduction

Details:

• Supercomputers analyze massive sensor data from urban traffic systems

• Quantum computing is used to optimize vehicle routing based on congestion risks and constraints

• Demonstration Results: Confirmed real-time effectiveness in route optimization

4. Hybrid Applications in Quantum Machine Learning

Example: Google Quantum AI’s Research on Quantum Machine Learning

• Supercomputer: Large-scale data pre-processing, quantum circuit design, parameter exploration

• Quantum Computer: Quantum feature mapping, partial classification tasks

Details:

• Supercomputers handle overall model design and hyperparameter optimization

• Quantum computers execute complex feature extraction and non-linear transformations for part of the process

• Results: Demonstrated performance improvements over conventional models, albeit on a small scale

5. Fault-Tolerant Quantum Algorithm Development

Example: RIKEN’s Fugaku + Quantum Hardware Integration Initiative

• Supercomputer (Fugaku): Quantum circuit simulation, verification of error correction algorithms

• Quantum Computer: Hardware testing, real-world environment verification

Details:

• Supercomputers simulate quantum circuit behavior and test error resilience

• Real quantum hardware is used to confirm operation and iteratively improve designs

• Goal: Build a stable, fault-tolerant quantum computing foundation

Key Considerations

• Large-scale, fully independent quantum computing remains challenging with current technology

• Realistic solutions rely on hybrid systems that leverage “partial advantages” of quantum computing

• Communication interfaces and achieving real-time system integration remain technological challenges for the future

Recommended References for Understanding Hybrid Use of Quantum Computers and Supercomputers

1. “Quantum Computing for Computer Scientists“
   Noson S. Yanofsky, Mirco A. Mannucci
   → Comprehensive introduction to quantum computing with a focus on integration with classical computing systems. Suitable for computer scientists.
   ISBN: 978-0521879965

2. “Quantum Computing: An Applied Approach“
   Jack D. Hidary
   → Focuses on practical applications, including hybrid quantum-classical algorithms (VQE, QAOA), with Python examples.
   ISBN: 978-3030239213

3. “Quantum Computation and Quantum Information“
   Michael A. Nielsen, Isaac L. Chuang
   → The standard graduate-level textbook. Covers both theory and foundational concepts useful for understanding hybrid architectures.
   ISBN: 978-1107002173

4. D-Wave Official Resources
   → Focus on quantum annealing and hybrid quantum-classical computing for optimization problems.

5. IBM Quantum Experience & Qiskit Documentation
   → Provides cloud access to quantum computers, examples of hybrid workflows, and open-source tools for hybrid algorithms like VQE, QAOA.

6. “Quantum simulations of materials on near-term quantum computers
   → Demonstrates quantum chemistry simulations using hybrid quantum-classical systems, including Google’s experiments.

7. “Hybrid quantum-classical machine learning for generative chemistry and drug design“

8. “Classification of Hybrid Quantum-Classical Computing“

• Fujitsu Quantum-Inspired Digital Annealer
  → While not a true quantum computer, this architecture demonstrates classical acceleration of quantum-like optimization, often integrated with supercomputers.

• Google Sycamore + Classical Supercomputer Experiments
  → Used hybrid approaches to demonstrate quantum supremacy and explore chemistry and optimization tasks.