Qubrid offers Quantum Computing consulting and professional services to commercial, government, educational and independent research labs. Our Quantum and AI research and software development is dedicated to creating advanced quantum solutions tailored to meet our client’s unique needs. With expertise spanning diverse domains such as portfolio optimization, predictive modeling, generative AI, logistics, and complex simulations, our skilled team designs specialized quantum algorithms that address each project’s distinct challenges.
We can help you with cutting-edge optimization algorithms such as:
Quantum Generative Adversarial Networks (QGANs) – We are pioneers in the field of quantum machine learning, notably Quantum Generative Adversarial Networks (QGANs), being one of the first companies to explore its potential. We believe that Quantum is going to accelerate AI to unparalleled proportions and we can help you with your Generative AI and Machine Learning applications in a Hybrid Quantum-Classical environment.
VQE (Variational Quantum Eigensolver) is a technique used to estimate the lowest eigenvalue of a Hamiltonian, which represents the energy levels of a quantum system. The lowest eigenvalue corresponds to the ground state energy, which is the most stable state of the system. VQE provides an upper bound for this lowest eigenvalue by using variational principles, allowing us to approximate the value even when finding the exact value is difficult. The VQE-derived upper bound helps us estimate the ground state energy within a certain range, aiding in understanding and predicting the behavior of quantum systems.
Quantum Approximate Optimization Algorithm (QAOA) is a quantum computing method for solving optimization problems. It involves using two types of operators, generated from the cost Hamiltonian (HC) and the mixer Hamiltonian (HB), to evolve a quantum state towards an optimal solution. The state |γ,β⟩ is constructed by repeatedly applying these operators to an initial state |s⟩. The angles γ and β control the influence of the operators, guiding the exploration of the energy landscape. QAOA’s iterative optimization process refines the angles to improve the energy of the quantum state until convergence. The success of QAOA relies on the interaction between the cost and mixer Hamiltonians, the evolution operators, and quantum properties like superposition and entanglement, enabling efficient exploration of complex energy landscapes to approximate optimization solutions.
Quantum Annealing employs quantum tunneling to optimize and search for the global best solution in optimization problems. Despite being heuristic, it lacks a guarantee to find the absolute best solution. Quantum Annealer machines address optimization problems using Quadratic Unconstrained Binary Optimization (qubo) modeling. Even combinatorial optimization problems, featuring discrete variable values and constraints, can be cast as qubo problems to leverage the capabilities of Quantum Annealing.
Quadratic Unconstrained Binary Optimization (QUBO), also referred to as unconstrained binary quadratic programming (UBQP), presents a type of problem in combinatorial optimization that finds applications in various fields such as finance, economics, and machine learning. QUBO is a tough problem known as NP hard, and it finds relevance in solving classical problems from theoretical computer science, including tasks like maximum cut, graph coloring, and the partition problem. These problems are transformed into QUBO format. This transformation is also used in machine learning tasks like support-vector machines, clustering, and probabilistic graphical models. Additionally, due to its close relationship with Ising models, QUBO plays a key role in adiabatic quantum computation, where it’s solved using a physical process called quantum annealing.
Beyond algorithm design, we offer custom quantum research opportunities for clients to explore innovative quantum techniques. By leveraging our expertise, clients can evaluate applications and identify where quantum outperforms classical systems. We also assist in transitioning from classical machine learning to quantum applications, benchmarking quantum solutions against classical methods, and refining strategies. Our commitment to knowledge sharing is reflected in user-friendly training resources, facilitating the smooth adoption of quantum technologies.
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