Glossary

13.4 Running algorithms on real quantum hardware

4 min read•july 23, 2024

Quantum hardware is now accessible through cloud platforms like and . These services let users quantum circuits on real quantum processors, opening up exciting possibilities for experimentation and algorithm development.

Using quantum hardware involves configuring circuits, submitting jobs, and monitoring execution. Understanding hardware specs, limitations, and optimization techniques is crucial for getting reliable results and maximizing the potential of quantum algorithms on real devices.

Accessing and Using Quantum Hardware

Cloud quantum computing platforms

Top images from around the web for Cloud quantum computing platforms

  • IBM Q quantum computer | Lars Plougmann | Flickr View original

    Is this image relevant?

  • IBM Q quantum computer | Lars Plougmann | Flickr View original

    Is this image relevant?

1 of 2

Top images from around the web for Cloud quantum computing platforms

  • IBM Q quantum computer | Lars Plougmann | Flickr View original

    Is this image relevant?

  • IBM Q quantum computer | Lars Plougmann | Flickr View original

    Is this image relevant?

1 of 2
  • IBM Quantum Experience enables users to access and experiment with real quantum computers through the cloud
    • Create an account and log in to the platform to get started
    • Explore the available quantum processors (e.g., ibmq_qasm_simulator, ibmq_16_melbourne) and their specifications such as number of , , and gate fidelities
    • Use the SDK, a Python-based framework, to interact with IBM Quantum Experience and write quantum circuits
  • Amazon Braket is a fully managed quantum computing service that provides access to a variety of quantum hardware devices
    • Set up an AWS account and access the Amazon Braket service through the AWS Management Console
    • Familiarize yourself with the available quantum processors from different providers (e.g., , , ) and their unique characteristics
    • Use the Amazon Braket SDK, available in Python, to write and submit quantum circuits tailored to the selected hardware

Quantum circuit submission process

  • configuration involves defining the desired and measurements using the appropriate SDK
    • Define the using the provided functions and classes in the SDK (e.g., 
      QuantumCircuit
      in Qiskit, 
      [Circuit](https://www.fiveableKeyTerm:Circuit)
      in Amazon Braket)
    • Specify the desired or QPU to run the circuit on, considering factors like number of qubits, connectivity, and available gate set
    • Set the number of , which determines how many times the circuit will be executed on the hardware to gather statistics
  • Submitting quantum circuits to the selected hardware is done through the SDK's built-in methods
    • Use the SDK's methods (e.g., 
      [execute](https://www.fiveableKeyTerm:execute)
      in Qiskit, 
      run
      in Amazon Braket) to submit the configured quantum circuit to the chosen hardware
    • Handle any necessary authentication or access token management required by the platform
    • Monitor the status of the submitted job (e.g., queued, running, completed) until it finishes execution

Monitoring and Optimizing Quantum Algorithms

Quantum algorithm execution monitoring

  • Job monitoring involves keeping track of the progress and status of the submitted quantum circuit execution
    • Check the status of the submitted job using the SDK's job management tools (e.g., 
      job.status()
      in Qiskit, 
      job.state()
      in Amazon Braket)
    • Retrieve the results once the job is completed, which may include the measured outcomes, , or other relevant data
  • Interpreting results requires understanding the format and meaning of the data returned by the quantum hardware
    • Understand the format of the returned results, such as counts (histogram of measured outcomes), (amplitudes of quantum states), or (description of mixed states)
    • Analyze the distribution of measured outcomes and their associated probabilities to gain insights into the quantum algorithm's behavior
    • Compare the obtained results with the expected theoretical outcomes to validate the correctness and performance of the quantum algorithm

Real quantum hardware performance

  • Hardware specifications play a crucial role in determining the capabilities and limitations of quantum processors
    • Understand the number of qubits available in the quantum processor, as it determines the size of the quantum systems that can be simulated
    • Consider the connectivity between qubits, which affects the ability to perform multi-qubit gates and influences the mapping of logical qubits to physical qubits
    • Familiarize yourself with the supported by the hardware, as it impacts the decomposition and translation of quantum circuits
  • Limitations and noise inherent in real quantum hardware can significantly affect the performance and reliability of quantum algorithms
    • Recognize the impact of , which causes the quantum states to lose their coherence over time, leading to errors and loss of information
    • Assess the of the quantum operations and measurements, as imperfect gates and readout errors can introduce inaccuracies in the results
    • Evaluate the scalability and reliability of the quantum hardware for running specific algorithms, considering factors like , , and qubit connectivity

Optimization for quantum architectures

  • Hardware-specific optimization techniques can be employed to adapt quantum algorithms to the characteristics of the target quantum processor
    • Tailor the quantum algorithm to leverage the connectivity and native gate set of the hardware, minimizing the need for additional or gate decompositions
    • Minimize the number of required SWAP operations for non-adjacent qubit interactions, as they introduce overhead and potential errors
    • Exploit any available hardware-specific features or instructions that can enhance the performance or reduce the complexity of the quantum algorithm
  • Error mitigation techniques aim to reduce the impact of noise and errors on the quantum algorithm's execution
    • Implement , which encode logical qubits into multiple physical qubits to detect and correct errors
    • Use techniques, such as pulse sequences or gate operations, to reduce the effect of decoherence on the qubits
    • Apply methods, such as calibration or post-processing, to improve the accuracy of the measured outcomes
  • and compilation optimize the quantum circuit for efficient execution on the target hardware
    • Optimize the quantum circuit by minimizing the and depth, reducing the overall execution time and exposure to noise
    • Transpile the circuit to the native gate set of the target hardware, decomposing high-level gates into the supported elementary operations
    • Compile the circuit to a low-level instruction set (e.g., pulse-level instructions) that is compatible with the specific quantum processor architecture

Key Terms to Review (32)

Amazon Braket: Amazon Braket is a fully managed quantum computing service provided by Amazon Web Services (AWS) that allows developers to build, test, and run quantum algorithms on various quantum computers. This service facilitates access to different quantum hardware providers, enabling users to experiment with quantum circuits and algorithms in a cloud-based environment. By integrating classical and quantum computing resources, it supports the development of practical applications in quantum computing.
Circuit: In quantum computing, a circuit is a sequence of quantum gates and measurements applied to qubits, designed to perform a specific computation. These circuits are the foundation of running algorithms on quantum hardware, as they provide a structured way to manipulate qubits and achieve desired outcomes through quantum operations.
Circuit Depth: Circuit depth refers to the minimum number of sequential time steps required to execute a quantum circuit from start to finish, taking into account the dependencies between quantum gates. This concept is crucial for understanding how efficiently a quantum algorithm can be executed on quantum hardware. Circuit depth affects not only the overall time complexity of quantum algorithms but also the error rates in quantum computations, as longer circuits are more prone to decoherence and noise.
Circuit transpilation: Circuit transpilation is the process of transforming a quantum circuit into a form that is compatible with the specific architecture of a quantum computer. This transformation involves optimizing the circuit for efficiency, reducing errors, and ensuring that it fits within the constraints of the hardware. It connects to the running of quantum algorithms on real hardware by making sure that the intended operations can be executed accurately and efficiently on available quantum devices.
Coherence Times: Coherence times refer to the duration over which a quantum state can maintain its quantum properties, such as superposition and entanglement, before being disrupted by environmental interactions. This concept is crucial in the performance of quantum algorithms on actual hardware, particularly in determining how effectively information can be processed before it is lost due to decoherence. Understanding coherence times is essential for optimizing the design and operation of superconducting qubits, which are a leading technology in quantum computing.
Connectivity: Connectivity refers to the way qubits in a quantum computer are linked or interact with each other. High connectivity allows qubits to be entangled and influence one another, which is essential for executing complex quantum algorithms effectively. It impacts the design and performance of quantum circuits and is critical when running algorithms on real quantum hardware, as it determines how efficiently information can be transmitted between qubits.
Counts: In quantum computing, 'counts' refer to the number of times a specific measurement outcome is recorded after running a quantum algorithm or simulation. This term is crucial in understanding the statistical nature of quantum measurements, as counts represent how frequently particular results appear, helping researchers analyze the performance and reliability of algorithms on both simulated and real quantum hardware.
D-wave: D-Wave refers to a specific type of quantum computer that utilizes quantum annealing to solve optimization problems. Unlike traditional computers that use bits, D-Wave systems leverage qubits to perform calculations more efficiently for particular tasks. This approach allows D-Wave machines to tackle complex problems in various fields, demonstrating significant potential for advancements in technology and scientific research.
Decoherence: Decoherence is the process by which quantum systems lose their quantum behavior due to interactions with their environment, resulting in the transition from a coherent superposition of states to a classical mixture of states. This phenomenon plays a crucial role in understanding the limitations of quantum computing, as it can lead to the loss of information and the degradation of quantum states, impacting various aspects of quantum technology.
Density matrices: Density matrices are mathematical representations used to describe the statistical state of a quantum system, particularly when the system is in a mixed state rather than a pure state. They allow us to capture information about probabilities of different outcomes and provide insights into the coherence and entanglement of quantum states. This concept becomes crucial when running algorithms on real quantum hardware, as it helps account for imperfections and noise that can affect the results.
Dynamical Decoupling: Dynamical decoupling is a technique used in quantum computing to protect quantum states from decoherence by applying a sequence of rapid pulses or operations. This method helps to mitigate the effects of unwanted interactions with the environment, thereby preserving the coherence of qubits over time. By strategically timing these operations, it creates an effective way to maintain the integrity of quantum information during computations and experiments.
Error Rates: Error rates refer to the frequency of mistakes or inaccuracies that occur during quantum computations. These rates are crucial for assessing the reliability of quantum operations and algorithms, particularly in the context of universal quantum gates, running algorithms on real quantum hardware, and superconducting qubits. High error rates can significantly impact the fidelity of computations, making it essential to develop techniques for error correction and mitigation.
Execute: In the context of quantum computing, 'execute' refers to the process of running a quantum algorithm on quantum hardware to obtain results. This involves taking a well-defined set of instructions, often expressed in the form of a quantum circuit, and translating them into operations that can be performed by physical quantum bits (qubits) in a quantum processor. The execution process is crucial as it translates theoretical quantum algorithms into practical outcomes, allowing researchers and developers to test and validate their computational approaches on real devices.
Fidelity: Fidelity in quantum computing refers to the degree to which a quantum state or operation accurately reflects or reproduces the intended quantum state or operation. It is a crucial measure of performance and reliability, particularly when assessing the effectiveness of quantum technologies, protocols, and error correction mechanisms.
Gate count: Gate count refers to the total number of quantum gates used in a quantum circuit to implement a specific algorithm or computation. This metric is crucial as it provides insight into the complexity and resource requirements of a quantum algorithm, influencing both its performance on simulators and real quantum hardware. A lower gate count often leads to more efficient circuits that are easier to run on existing quantum devices, which typically have limitations in terms of gate fidelity and coherence time.
IBM Quantum Experience: IBM Quantum Experience is an online platform that provides users access to IBM's quantum computers and a suite of tools for quantum programming and research. This platform allows researchers, developers, and enthusiasts to experiment with quantum algorithms and run them on real quantum hardware, making it a significant player in the current landscape of quantum computing technologies. By offering cloud-based access to quantum systems, it enables a wide range of users to engage with quantum computing without needing specialized hardware.
IonQ: ionQ is a leading company in the field of quantum computing that focuses on developing and commercializing quantum hardware based on trapped ion technology. Their systems utilize individual ions trapped in electromagnetic fields to perform computations, enabling the execution of complex quantum algorithms with high fidelity and scalability. This technology is significant for running algorithms on real quantum hardware, providing users access to quantum computing resources for practical applications.
Job status: Job status refers to the current state or progress of a quantum computing job as it is executed on quantum hardware. Understanding job status is essential when running algorithms, as it provides feedback on whether a job is pending, running, completed, or has failed, thus allowing users to monitor and manage their computational tasks effectively.
Native gate set: A native gate set refers to the collection of quantum gates that are directly supported by a specific quantum hardware platform, allowing for the execution of quantum algorithms on that device. Each type of quantum computer has its own unique native gate set, which influences how efficiently algorithms can be implemented and executed. Understanding the native gate set is crucial for optimizing quantum circuits and maximizing performance on real quantum hardware.
Qiskit: Qiskit is an open-source quantum computing software development framework that allows users to create, simulate, and run quantum algorithms on real quantum computers. It serves as a bridge between classical computing and quantum computing, enabling programmers to work with quantum circuits and operations through a user-friendly interface. Qiskit is widely used for educational purposes and in research settings to explore the potential of quantum technologies.
Quantum Circuit: A quantum circuit is a model for quantum computation that uses qubits and quantum gates to perform operations on quantum data. It represents a sequence of operations applied to the qubits, enabling the implementation of quantum algorithms and manipulation of quantum states. This concept is crucial for understanding how quantum algorithms are structured, as well as their execution on quantum processors.
Quantum circuit: A quantum circuit is a model for quantum computation in which a sequence of quantum gates is applied to qubits, the fundamental units of quantum information. This framework allows for the representation and execution of quantum algorithms by manipulating qubits through various operations, enabling the exploration of complex computational problems that classical circuits cannot efficiently solve.
Quantum Error Correction Codes: Quantum error correction codes are methods used to protect quantum information from errors due to decoherence and other quantum noise. These codes are essential for preserving the integrity of quantum states, allowing for reliable quantum computation and communication by enabling the detection and correction of errors without directly measuring the quantum state, which would collapse it. They leverage the principles of superposition and entanglement to encode information in such a way that it can be recovered even when certain qubits are disturbed.
Quantum operations: Quantum operations are mathematical processes that act on quantum states, transforming them and capturing the dynamics of quantum systems. They are essential for understanding how quantum information is manipulated, including concepts like measurement, evolution, and decoherence. These operations can be represented as completely positive maps, which help ensure that probabilities remain valid even after transformation.
Quantum processor: A quantum processor is a computational device that manipulates quantum bits, or qubits, to perform calculations based on the principles of quantum mechanics. Unlike classical processors that use bits as the smallest unit of data, quantum processors leverage phenomena like superposition and entanglement to handle complex problems much more efficiently. This technology enables the execution of quantum algorithms that can solve certain types of problems exponentially faster than their classical counterparts.
Qubits: Qubits are the basic units of quantum information, analogous to classical bits but with the unique ability to exist in multiple states simultaneously. This superposition allows qubits to represent both 0 and 1 at the same time, enabling quantum computers to process vast amounts of information more efficiently than classical computers. Qubits also exhibit entanglement, where the state of one qubit can depend on the state of another, making them essential for quantum algorithms and real quantum hardware operations.
Readout error mitigation: Readout error mitigation refers to techniques used to correct or minimize errors that occur during the measurement process of quantum bits (qubits) in quantum computing. These errors can significantly affect the accuracy of computational results when running algorithms on actual quantum hardware. Effective readout error mitigation is crucial for improving the reliability of quantum computations and making them more comparable to classical computing results.
Rigetti: Rigetti Computing is a company specializing in quantum computing technology, focused on building quantum processors and providing cloud access to these quantum systems. They aim to develop practical quantum applications and run algorithms on real quantum hardware, pushing the boundaries of what quantum computing can achieve in various fields.
Run: In the context of quantum computing, 'run' refers to the execution of a quantum algorithm on a quantum computer. This process involves loading the algorithm into the quantum system, initializing qubits, and performing quantum gates to manipulate those qubits. Each run produces a result that can be measured, providing insights into the behavior of the algorithm and its effectiveness in solving specific problems.
Shots: In the context of quantum computing, 'shots' refer to the number of times a quantum algorithm is run on a quantum computer to gather statistical results. Since quantum measurements can yield probabilistic outcomes due to the nature of quantum mechanics, executing multiple shots allows researchers to build a more accurate picture of the system's behavior and performance. Each shot produces a result that can be analyzed to understand the overall trends and probabilities associated with the algorithm being tested.
Statevectors: Statevectors are mathematical representations of the quantum state of a system, encapsulating all possible information about that system. They exist in a complex vector space and are critical for describing how quantum states evolve over time. In the context of quantum computing, statevectors are fundamental to running algorithms on real quantum hardware, as they illustrate the superposition and entanglement characteristics that enable quantum computation.
Swap operations: Swap operations are quantum gates used to exchange the states of two qubits in a quantum circuit. They are essential for manipulating quantum information and play a crucial role in ensuring that algorithms can efficiently utilize qubit entanglement and superposition, especially when running on real quantum hardware. These operations help facilitate the interaction between qubits, enabling complex quantum computations.
© 2024 Fiveable Inc. All rights reserved.
AP® and SAT® are trademarks registered by the College Board, which is not affiliated with, and does not endorse this website.
Back
Practice QuizGlossary
Practice QuizGlossary
Next guide