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Quantum Open Source: Accelerate Research with Classiq’s IDE
Imagine a future where diseases are cured faster, financial markets are accurately predicted, and encrypted data becomes virtually unbreakable.
This is the reality we want to achieve.
Quantum technologies are set to revolutionize fields from material science to cryptography.
However, for researchers diving into the world of quantum technology, the journey has many challenges. Quantum computing is an extremely broad field that demands a unique blend of skills, ranging from physics to coding to hardware knowledge.
This is a lot of complexity to tackle head-on.
The non-intuitive nature of quantum mechanics, combined with a steep learning curve, can make quantum research seem difficult to grasp. This is where initiatives like the Quantum Open Source Foundation (QOSF) bridge the educational gap by offering mentorship for implementing quantum algorithms.
But what if we can accelerate quantum research even more?
Here, we take some of the incredible projects built by students in the QOSF mentorship program and show how Classiq can help the learner cut down development time and the learning curve and help researchers dive into quantum computing even faster.
Revolutionizing Quantum Research with Classiq’s Platform
The Classiq platform offers a Quantum Software Development Kit (SDK) and an Integrated Development Environment (IDE) that change the development process flow. This environment allows researchers to transform their research into quantum code more efficiently.
We need to avoid writing the same algorithms from scratch. Implementations tend to be poorly documented, sometimes irreproducible, and subject to code rot. Software that’s not backward-compatible can completely destroy a researcher’s time, forcing them to rewrite and debug code with every software update and every hardware change.
Kickstart with a Library of Algorithms
The library of pre-written boilerplate code allows researchers to focus on learning and research instead of rewriting one thousand lines of basic code for different hardware platforms. This reduces that steep learning curve and puts the focus where it should belong—on innovation rather than getting bogged down by the complexities of quantum programming syntax.
Optimized Code for Efficiency
One of the great benefits of using Classiq is the amount of time saved. The platform's pre-optimized code eliminates the need for researchers to spend countless hours writing and debugging basic algorithms. This allows them to invest more time in exploring innovative concepts and applications, accelerating the pace of discovery in quantum research.
Adaptable Code for Diverse Quantum Hardware
Another key advantage of Classiq’s platform is the compilation and transpilation loops to adapt to different quantum hardware systems. Whether it's IBM's chips, connecting through AWS, or other cloud providers, Classiq’s code is automatically transpiled to optimize for different hardware specifications. This flexibility is crucial in a field where hardware changes quickly on the chip level and with daily calibration.
Case Studies from the QOS Projects
The Quantum Open Source Foundation (QOSF) is critical in mentoring the next generation of quantum researchers. These open-source projects range from quantum optimization to advanced simulations and span the breadth of quantum computing applications.
With the Classiq IDE, the journey from concept to algorithm is smoother for newcomers or seasoned academic researchers. Here, we discuss a few incredible projects from previous cohorts of the QOSF mentorship.
Let's explore how Classiq's platform could help these researchers save time and energy in boilerplate code and accelerate their research!
Project 1: Quantum Natural Gradient and VQE Optimization
The first project we'll examine focuses on applying the Quantum Natural Gradient to accelerate the Variational Quantum Eigensolver (VQE) algorithm and improve its interaction with classical computers.
VQE is crucial in quantum chemistry and material science to find the ground state of molecular systems.
The comparison between classical and quantum natural gradients was a key focus. Classiq's pre-made VQE algorithm, highly tunable and ready-to-use, could have significantly streamlined this process. By integrating Classiq, the project could have focused on specifying gradient techniques, reducing the complexity of the code.
The LiH VQE 4-Qubit Example below outlines how Classiq’s Hamiltonian generation and runtime specifications could have simplified the original implementation. Though the original Hamiltonian generation is efficient, it also requires a custom LiH definition file.
We can do much of the heavy lifting with the synthesis configuration tab in Classiq.
In the Classiq IDE, the Synthesis Configuration allows us to set the atoms and configuration purely visually, without needing code.
Log into the platform, select the atoms from the downtown, and click “Synthesize” to generate the circuit.
While this is implemented for a 4-qubit system, you will also need a custom runtime definition if coding for a specific hardware platform.
In the synthesis model, we can also add additional preferences, including:
- Optimize for depth or width
- Change basis gates
- Specify a max gate count
- Add a custom connectivity map
And even output the synthesized circuit in different languages, including qasm, Qsharp, Cirq, and others, to allow for development across any platform.
As discussed in a previous article in this series, The Key to Full Stack Quantum Computing, this platform is also adaptable to different chips without requiring manual effort. This could be transpiled to different hardware to compare results between different chips.
Read more about Ansatz Implementation here. The textual model from the IDE and the SDK code are provided.
Project 2: Financial Portfolio Rebalancing with QAOA
Here, we look at a project where quantum computing meets finance: using Variational Algorithms, like the Quantum Approximate Optimization Algorithm (QAOA), for financial portfolio rebalancing. This project shows the impact of quantum computing in optimizing complex financial models, examines both soft and hard constraints, and uses different optimization algorithms, including gradient descent, cross-entropy, and SciPy optimizers.
This is a lot of manual coding work for a researcher!
Classiq has a Portfolio Optimization Built-In Algorithm:
Using the IDE, the max width, depth, and gate count can be added as a constraint and the system will optimize across.
The objective function can also be manually edited, allowing for greater control.
After synthesizing, the researcher could also transfer this code to continue development - for example, tweaking gradient descent.
This automation also makes quantum computing more accessible. By simplifying the technical barriers, the platform opens the door for scientists and engineers outside of quantum computing, including those who may not have deep coding expertise in hardware or quantum algorithms. Here, the machine learning researcher can dive into their zone of genius without worrying about the nuances of quantum computing.
Streamlining Quantum Innovation with Classiq’s Advanced Tools
Classiq enables researchers to focus on what truly matters: innovation. By eliminating repetitive coding tasks, like rewriting standard code and then changing it for every different piece of quantum hardware and software update, researchers can concentrate on developing new algorithms and exploring new applications in quantum computing.
This shift from coding to creativity enhances the quality of research but also attracts new talent to the field, including those who may be more conceptually inclined or come from a non-computing background. This inclusivity is essential for building a quantum computing community with different domain expertise, which leads to more collaboration and breakthroughs for quantum technology.
Exploring the Quantum Frontier
In the 70s, we opened doors for people without a physics background to be able to use classical computers. It’s time to do that again with quantum computers.
Looking ahead, the advancements in Classiq’s platform are exciting not just for their technical details but also for getting more domain experts hands-on with quantum computing. By streamlining the development process, bridging the gap between quantum and classical computing, and empowering researchers through automation, the future of quantum computing is accelerated.
For researchers and students eager to dive into the world of quantum computing, Classiq’s platform offers a quicker start for innovation. Instead of coding circuits, you can work through the intuition of quantum computing. Discover more about Classiq and QOSF projects through these links and resources, and explore, innovate, and contribute to a field shaping our future.
Imagine a future where diseases are cured faster, financial markets are accurately predicted, and encrypted data becomes virtually unbreakable.
This is the reality we want to achieve.
Quantum technologies are set to revolutionize fields from material science to cryptography.
However, for researchers diving into the world of quantum technology, the journey has many challenges. Quantum computing is an extremely broad field that demands a unique blend of skills, ranging from physics to coding to hardware knowledge.
This is a lot of complexity to tackle head-on.
The non-intuitive nature of quantum mechanics, combined with a steep learning curve, can make quantum research seem difficult to grasp. This is where initiatives like the Quantum Open Source Foundation (QOSF) bridge the educational gap by offering mentorship for implementing quantum algorithms.
But what if we can accelerate quantum research even more?
Here, we take some of the incredible projects built by students in the QOSF mentorship program and show how Classiq can help the learner cut down development time and the learning curve and help researchers dive into quantum computing even faster.
Revolutionizing Quantum Research with Classiq’s Platform
The Classiq platform offers a Quantum Software Development Kit (SDK) and an Integrated Development Environment (IDE) that change the development process flow. This environment allows researchers to transform their research into quantum code more efficiently.
We need to avoid writing the same algorithms from scratch. Implementations tend to be poorly documented, sometimes irreproducible, and subject to code rot. Software that’s not backward-compatible can completely destroy a researcher’s time, forcing them to rewrite and debug code with every software update and every hardware change.
Kickstart with a Library of Algorithms
The library of pre-written boilerplate code allows researchers to focus on learning and research instead of rewriting one thousand lines of basic code for different hardware platforms. This reduces that steep learning curve and puts the focus where it should belong—on innovation rather than getting bogged down by the complexities of quantum programming syntax.
Optimized Code for Efficiency
One of the great benefits of using Classiq is the amount of time saved. The platform's pre-optimized code eliminates the need for researchers to spend countless hours writing and debugging basic algorithms. This allows them to invest more time in exploring innovative concepts and applications, accelerating the pace of discovery in quantum research.
Adaptable Code for Diverse Quantum Hardware
Another key advantage of Classiq’s platform is the compilation and transpilation loops to adapt to different quantum hardware systems. Whether it's IBM's chips, connecting through AWS, or other cloud providers, Classiq’s code is automatically transpiled to optimize for different hardware specifications. This flexibility is crucial in a field where hardware changes quickly on the chip level and with daily calibration.
Case Studies from the QOS Projects
The Quantum Open Source Foundation (QOSF) is critical in mentoring the next generation of quantum researchers. These open-source projects range from quantum optimization to advanced simulations and span the breadth of quantum computing applications.
With the Classiq IDE, the journey from concept to algorithm is smoother for newcomers or seasoned academic researchers. Here, we discuss a few incredible projects from previous cohorts of the QOSF mentorship.
Let's explore how Classiq's platform could help these researchers save time and energy in boilerplate code and accelerate their research!
Project 1: Quantum Natural Gradient and VQE Optimization
The first project we'll examine focuses on applying the Quantum Natural Gradient to accelerate the Variational Quantum Eigensolver (VQE) algorithm and improve its interaction with classical computers.
VQE is crucial in quantum chemistry and material science to find the ground state of molecular systems.
The comparison between classical and quantum natural gradients was a key focus. Classiq's pre-made VQE algorithm, highly tunable and ready-to-use, could have significantly streamlined this process. By integrating Classiq, the project could have focused on specifying gradient techniques, reducing the complexity of the code.
The LiH VQE 4-Qubit Example below outlines how Classiq’s Hamiltonian generation and runtime specifications could have simplified the original implementation. Though the original Hamiltonian generation is efficient, it also requires a custom LiH definition file.
We can do much of the heavy lifting with the synthesis configuration tab in Classiq.
In the Classiq IDE, the Synthesis Configuration allows us to set the atoms and configuration purely visually, without needing code.
Log into the platform, select the atoms from the downtown, and click “Synthesize” to generate the circuit.
While this is implemented for a 4-qubit system, you will also need a custom runtime definition if coding for a specific hardware platform.
In the synthesis model, we can also add additional preferences, including:
- Optimize for depth or width
- Change basis gates
- Specify a max gate count
- Add a custom connectivity map
And even output the synthesized circuit in different languages, including qasm, Qsharp, Cirq, and others, to allow for development across any platform.
As discussed in a previous article in this series, The Key to Full Stack Quantum Computing, this platform is also adaptable to different chips without requiring manual effort. This could be transpiled to different hardware to compare results between different chips.
Read more about Ansatz Implementation here. The textual model from the IDE and the SDK code are provided.
Project 2: Financial Portfolio Rebalancing with QAOA
Here, we look at a project where quantum computing meets finance: using Variational Algorithms, like the Quantum Approximate Optimization Algorithm (QAOA), for financial portfolio rebalancing. This project shows the impact of quantum computing in optimizing complex financial models, examines both soft and hard constraints, and uses different optimization algorithms, including gradient descent, cross-entropy, and SciPy optimizers.
This is a lot of manual coding work for a researcher!
Classiq has a Portfolio Optimization Built-In Algorithm:
Using the IDE, the max width, depth, and gate count can be added as a constraint and the system will optimize across.
The objective function can also be manually edited, allowing for greater control.
After synthesizing, the researcher could also transfer this code to continue development - for example, tweaking gradient descent.
This automation also makes quantum computing more accessible. By simplifying the technical barriers, the platform opens the door for scientists and engineers outside of quantum computing, including those who may not have deep coding expertise in hardware or quantum algorithms. Here, the machine learning researcher can dive into their zone of genius without worrying about the nuances of quantum computing.
Streamlining Quantum Innovation with Classiq’s Advanced Tools
Classiq enables researchers to focus on what truly matters: innovation. By eliminating repetitive coding tasks, like rewriting standard code and then changing it for every different piece of quantum hardware and software update, researchers can concentrate on developing new algorithms and exploring new applications in quantum computing.
This shift from coding to creativity enhances the quality of research but also attracts new talent to the field, including those who may be more conceptually inclined or come from a non-computing background. This inclusivity is essential for building a quantum computing community with different domain expertise, which leads to more collaboration and breakthroughs for quantum technology.
Exploring the Quantum Frontier
In the 70s, we opened doors for people without a physics background to be able to use classical computers. It’s time to do that again with quantum computers.
Looking ahead, the advancements in Classiq’s platform are exciting not just for their technical details but also for getting more domain experts hands-on with quantum computing. By streamlining the development process, bridging the gap between quantum and classical computing, and empowering researchers through automation, the future of quantum computing is accelerated.
For researchers and students eager to dive into the world of quantum computing, Classiq’s platform offers a quicker start for innovation. Instead of coding circuits, you can work through the intuition of quantum computing. Discover more about Classiq and QOSF projects through these links and resources, and explore, innovate, and contribute to a field shaping our future.
About "The Qubit Guy's Podcast"
Hosted by The Qubit Guy (Yuval Boger, our Chief Marketing Officer), the podcast hosts thought leaders in quantum computing to discuss business and technical questions that impact the quantum computing ecosystem. Our guests provide interesting insights about quantum computer software and algorithm, quantum computer hardware, key applications for quantum computing, market studies of the quantum industry and more.
If you would like to suggest a guest for the podcast, please contact us.
