What is Hybrid quantum computing?
The chosen industry phrase for the straightforward concept of a quantum computer and a classical computer cooperating to solve a problem is “hybrid quantum computing.”
This essentially sums up quantum computing: practically every part of operating a quantum computer depends on classical processors. All the way up to the cloud computers that compile, optimize, and transform user-submitted programs into something that can run on to hardware, from the system that coordinates the computer’s numerous subcomponents and executes the quantum gates to the hardware and software that measures and interprets the results of a computation.
Hybrid quantum computing Architectures
The integration of classical and quantum processes is growing as quantum technology develops and advances. Microsoft has created a four-stage taxonomy to identify progressively more complex hybrid quantum computing levels.
Batch quantum computing
Local clients are used in batch quantum architecture to define circuits. The circuits are then sequentially submitted as jobs to the quantum processing unit (QPU), which returns the results to the client. By combining several circuits into a single job, the wait time between job submissions to the QPU is eliminated, allowing the program to run more quickly overall. Simple quantum phase estimation and Shor’s algorithm are two examples of issues that can benefit from batch quantum computing.
Interactive quantum computing
This model reduces the latency between the client and QPU by moving the client’s compute resource to the cloud. This makes it possible to run the quantum circuit repeatedly with various parameters. On the quantum computer, jobs can be logically organized into a single session and prioritized as a group. Qubit states do not remain between jobs, even if sessions enable longer running problems and shorter queue times. This method can be used to issues such as quantum approximation optimization algorithms (QAOA) and variational quantum eigensolvers (VQE).
Integrated quantum computing
Physical qubits can be coherent while performing classical computations to the close coupling between the classical and quantum structures in integrated quantum computing. This architecture is an important step forward, allowing quantum programs to transcend circuits, even if it is still constrained by qubit life and error correction. Common programming elements like loops may now be used in quantum programs to optimize and reuse qubits, perform mid-circuit measurements, and modify circuits in real-time. Machine learning and adaptive phase estimation are two situations where this methodology can be useful.
Distributed quantum computing
Once to have scalable quantum computers with logical qubits, extended qubit lifetimes, and robust error correction, it will be able to unleash distributed quantum computing architectures. Logical qubits and classical computation will coexist in this system. Logical qubits’ extended coherence time will allow for complicated and dispersed computation across a variety of cloud resources, including QPUs, AI, and HPC.
It anticipate that this architecture will allow for solutions like the assessment of complete catalytic reactions by connecting QPUs made up of a high number of qubits with dispersed and potent cloud resources. It is anticipated that unlocking this architecture will have enormous commercial benefits and allow applications to address some of the most difficult issues confronting humanity, such carbon capture and medication development.
How does hybrid quantum computing work?
The fundamental tenet of hybrid quantum computing is that quantum computers and classical systems share a computational task, with the former serving as a co-processor. However, numerous contextual differences, such as different system designs and the kinds of activities that the engaged compute resources take over, may lead to varied interpretations of the term hybrid.
For instance, in order to provide remote access or regulate the physical functions of the quantum device, quantum computers are always dependent on classical computers. High-performance computing (HPC) systems are also used in some architectural approaches to speed up assistance activities including data preparation for quantum computers, compilation, and error mitigation.
Benefits of hybrid quantum computing
Only specific portions of a program will be executed by quantum computers. By utilising the advantages of both quantum and classical computing resources, hybrid algorithms make it easier to divide computational jobs between them. Recent hybrid quantum algorithms, such variationally quantum algorithms, guarantee that some jobs can be safely completed with existing quantum technology, even if many quantum algorithms require error correction and substantial physical resources.
What are the challenges of implementing hybrid quantum-classical algorithms?
Hybrid quantum-classical algorithm implementation presents a number of difficulties with regard to resource management, hardware and software compatibility, system design, and other areas. Innovative methods are needed to overcome these obstacles, ranging from software engineering and algorithm development to hardware design. Cooperation between government organizations, business, and academia will be essential to the development of hybrid quantum-classical computing technologies.
Applications of Hybrid Quantum Computing
The Applications described in article “Top Applications Of Quantum Computing For Enterprises” are identical to those of hybrid computing systems. Generally speaking, there are only few classifications.
- Finance, encompassing modelling, options pricing, and risk assessments
- Large dataset analysis and training acceleration are two aspects of machine learning (ML).
- Molecular simulations, drug discovery, catalyst design, battery and solar cell development, and weather forecasting are all examples of material science.
- General artificial intelligence (GAI) and natural language processing (NLP).
- Supply chains, inventory, financial portfolios, logistics routes, and site selection are all included in optimization (maximum independent set).
- There are plenty more use cases under investigation than this. However, once more, these use cases fit within these categories.
