What Is a Quantum Computing Simulator?

Quantum computing simulators are essential tools designed to emulate the behavior of quantum systems, offering researchers and enthusiasts an accessible platform to explore and develop quantum algorithms without the need for a physical quantum computer. There are many categories of quantum simulators but here we will be focusing on the most popular and common version—the state-vector (SV) simulators. These simulators store the entire quantum state and calculate its evolution, making them universal and ideal emulators of quantum computers.

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Types of Quantum Simulators

As a tool that mimics quantum systems, a quantum simulator allows researchers to explore quantum applications, debug algorithms, and understand qubit behavior—all without having to invest in quantum hardware. This is especially useful since access to actual quantum processors is still limited. Here are the different types of quantum simulators available today:  

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Analog Quantum Simulators

Analog quantum simulators use physical systems, such as cold atoms, trapped ions, or photons, to replicate quantum interactions. This makes them ideal for studying many-body systems, quantum phase transitions, and other phenomena in condensed matter physics. For example, researchers can use trapped ions to simulate spin systems or cold atoms in optical lattices. 

Analog simulators are highly specialized and limited to simulating specific types of quantum systems. This tends to reduce their flexibility. Scaling them to larger systems is also an issue due to control complexity. As the number of particles or qubits increases, maintaining precise control over the system becomes more complicated. 

Another challenge for analog systems is achieving high precision and resilience to environmental noise. Slight variations in parameters lead to inaccurate results, while noise disrupts the delicate quantum states such systems aim to simulate. Factors like these are why analog simulators are not a universal solution for quantum simulation—albeit being ideal for specific tasks. 

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Digital Quantum Simulators

Digital quantum simulators use quantum gates and algorithms to simulate quantum systems through discretization. They are programmable and can handle a broader range of problems compared to analog simulators, making them perfect for general-purpose quantum simulations. Researchers can use these simulators to implement quantum algorithms like Shor’s or Grover’s for various applications in optimization, cryptography, and chemistry.

On the other hand, digital simulators require a great deal of computational resources for large systems. As the number of qubits increases, the computational power needed to simulate them grows exponentially. This makes it challenging to model highly entangled systems, which are common in advanced quantum applications. Digital simulators also rely on classical hardware to mimic quantum behavior, which inherently limits their performance compared to fully quantum solutions.

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Hybrid Quantum Simulators

As the name suggests, hybrid quantum simulators combine classical computing and quantum systems to solve complex problems. Classical systems perform pre or post-processing, while quantum simulators handle specific calculations that require quantum speedups. These systems are especially useful for optimization, machine learning, and simulating quantum interactions that involve heavy computational loads.

Hybrid simulators depend on classical systems for computations that are not suitable for quantum processing. This can slow down performance and create bottlenecks. Integrating classical and quantum systems can also increase the risk of errors due to the complexity of coordination and synchronization. Despite their great potential, hybrid approaches are still in the early stages of development and need a lot of refinement to achieve seamless integration.

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Tensor Network Simulators

Tensor network simulators use tensor networks to represent quantum states—highly efficient for simulating many-body quantum systems. These simulators are great at studying systems with low entanglement, such as one-dimensional spin chains or certain condensed matter problems. They are the common choice for quantum chemistry and material science research.

While tensor network simulators are quite efficient for systems with low or moderate entanglement, they struggle with highly entangled or complex quantum systems. Their reliance on tensor representations means that storage and computational costs grow as the dimensionality or entanglement of the system increases. Although they are ideal for certain use cases, their lack of versatility makes them less useful in wider quantum computing applications like quantum chemistry or cryptography. 

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Online Quantum Computer Simulator Platforms

Quantum computer simulators are essential tools for experimenting with quantum algorithms and systems without needing a physical quantum computer. Let’s dive into some of the most popular simulators in the field.

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IBM Quantum Simulator

IBM offers a free quantum simulator, mostly geared toward educational use. While it’s great for learning, it comes with the downside of long queues and slower task completion, which might not suit researchers needing quick results.

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AWS Braket

On the other hand, AWS Braket offers a paid state-vector simulator with larger capacity and faster speeds compared to IBM. This makes it a better choice for users willing to pay for quicker and more dedicated simulations.

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BlueQubit Quantum Computing Simulator

BlueQubit offers both a free CPU version and a GPU version for advanced users who need top performance. To boost simulation speeds, it uses quantum computing software like Google's qsim and Nvidia’s cuQuantum library, making it a great option for large, complex quantum circuits.

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Performance Comparison

When comparing the performance of these simulators, we ran the same quantum circuits on all three platforms.

• IBM’s simulator supports up to 32 qubits, while AWS Braket can go up to 34 qubits.
• BlueQubit's BQ-CPU is approximately 13x faster than AWS Braket, and its BQ-GPU outperforms AWS Braket by a massive 230x. This speed boost comes from BlueQubit using more GPUs for each additional qubit, especially past 32 qubits.

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Quantum Simulator Benchmarks

Two popular hosted quantum simulators are provided by AWS Braket and IBM. Just like the one provided by Bluequbit, they are very easy to use and require 0-setup. Users only need to connect their account with the corresponding python SDK to submit large quantum circuits for simulation—much larger than what can be handled by an average laptop.

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Large Circuits

Below is a comparison of 32x32 circuit simulation runtime for these platforms.

As you can see, AWS’s simulator is more than twice faster than that of IBM. At the same time  BQ-CPU is faster than AWS Braket 12.7x times, and BQ-GPU - staggering 58x times.

We are using layer-structured random circuits for benchmarking, e.g. we alternate between 1-qubit and 2-qubit gates for each layer. We use "square" circuits, e.g. 24x24 or 32x32, where the first number is the number of qubits and the latter is the depth. The 1-qubit gates are chosen  randomly from this set: [X, Y, Z, H, S, T]. The 2-qubit gates are from: [CNOT,  CZ].

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Scaling with Qubits

To get a better holistic picture, it’s worth looking at simulation runtimes for more qubit sizes. So we take random circuits of the form 23x23, 24x24, ...., 35x35 for this benchmark.

We take into account the empirical observation that state vector simulators’ runtime should grow exponentially with the qubit size and linearly with the number of gates. Thus, we have a log scale for the Y-axis. We also show the runtime per gate, thus “normalizing” for the number of gates.

All four quantum computing simulators follow the 'exponential in qubits and linear in gates' rule. The reason for BQ-GPU being flat after 32 qubits is mentioned above, e.g., using twice as many GPUs. This makes the simulation more costly - but at the same time much faster.

This plot proves again that BlueQubit's GPU simulator is in a totally different league - as one would expect when comparing CPU vs GPU performance.

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Deep Circuits and Cost Benchmarks

Aside from benchmarking square circuits, it’s also useful to see how different quantum computing simulators perform on deeper circuits. One reason why we might expect larger speedups for deeper circuits is the following: 

There is a fixed cost for allocating the memory in a state vector simulator. For high-qubit, shallow circuits, this cost dominates the runtime. For deeper circuits, the actual simulation time per gate becomes the dominant factor, and unlike the memory allocation, that is where the speedup of GPU simulators lies. 

We saw earlier that a BQ-GPU simulator is 230x faster than AWS Braket’s SV1 on a 34x34 circuit. We have tried a 34x200 random circuit as well, and the difference jumped to 560x.

Below is a cost comparison for that circuit.

It’s also worth mentioning that if speed is not of the most importance, one could use BQ-CPU (that’s provided for FREE), and it would still be ~12.7x faster than AWS Braket SV1. More info on BlueQubit supported devices and their pricing can be found on our platform.

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Why are Quantum Simulators Important?

Quantum computer simulators hold immense importance in the present and near future as they allow for the development and testing of quantum algorithms, optimization techniques, and quantum error correction methods without the constraints of physical quantum hardware. As the field of quantum computing advances, simulators play a crucial role in training a new generation of quantum programmers and preparing the ground for the widespread adoption of quantum technology. Furthermore, they facilitate the exploration of novel applications and interdisciplinary research, helping to bridge the gap between quantum theory and real-world implementation, ultimately accelerating the arrival of the quantum era.

For a deeper dive into the quantum mechanics behind these simulators, check out our article on the quantum mechanical model.

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To Sum It up

BlueQubit's quantum computing simulators, leveraging the open-source qsim and cuQuantum libraries, provide a faster and more cost-effective alternative to other managed services. Their performance advantage renders them the practical choice for running large, high-depth circuits, outshining other platforms in the rapidly advancing field of quantum computing.

For more information on quantum data loading and quantum computing companies, visit our blog.

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FAQ

Is it possible to simulate a quantum computer?

Yes, it is possible to simulate a quantum computer using classical systems, but only for a limited number of qubits. Quantum simulators mimic quantum behavior by replicating qubit operations and quantum gates. As the number of qubits increases, however, so does the computational demand. This makes simulations impractical for larger systems. Quantum simulators come in handy for research, testing quantum algorithms, and trying out small-scale quantum applications before implementing them on actual quantum hardware.

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What is the best quantum simulator?

The best quantum simulator depends on the user’s needs. For beginners and researchers, IBM’s Qiskit and Google’s Cirq offer easy access to simulate quantum circuits. NVIDIA’s cuQuantum comes with high-performance simulation capabilities for advanced users looking to run large-scale simulations. BlueQubit also offers a simulation platform along with its Quantum Software as a Service (QSaaS), allowing users to access various QPUs and test quantum algorithms.

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