diff --git a/python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py b/python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py index bfe777e..1c7620c 100644 --- a/python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py +++ b/python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py @@ -14,7 +14,7 @@ import matplotlib import ray ray.shutdown() -ray.init() +ray.init(address="auto") from hops.util.logging_setup import logging_setup import logging @@ -42,6 +42,7 @@ def anti_zeno_engine( L_mat=qt.sigmax().full(), therm_methods=["fft", "fft"], terms=5, + sp_tol=1e-5, ): # τ_bath = 1 / ω_c τ_mod = 2 * np.pi / Δ @@ -125,7 +126,7 @@ def anti_zeno_engine( ) ) * np.sqrt(δ_init), - ConstantMatrix(np.zeros((2,2))), + ConstantMatrix(np.zeros((2, 2))), Shift( L, initializing_period + τ_off, @@ -149,8 +150,8 @@ def anti_zeno_engine( k_max=4, bcf_terms=[terms, terms], truncation_scheme="simplex", - driving_process_tolerances=[StocProcTolerances(1e-5, 1e-5)] * 2, - thermal_process_tolerances=[StocProcTolerances(1e-5, 1e-5)] * 2, + driving_process_tolerances=[StocProcTolerances(sp_tol, sp_tol)] * 2, + thermal_process_tolerances=[StocProcTolerances(sp_tol, sp_tol)] * 2, T=[T_c, T_h], L=[L] * 2, H=H, @@ -185,12 +186,12 @@ def anti_zeno_engine( ε=.05,#.1, ω_c=1, ε_couple=0.7, - n=5, + n=2, detune=.5, ω_0=20, - T_c=10, - T_h=40, - δ=[2.2, 1], + T_c=1e3, + T_h=1e4, + δ=[3.2, 1], γ=.2, switch_cycles=1, therm_initial_state=False, @@ -198,6 +199,7 @@ def anti_zeno_engine( ε_init=0.1, terms=6, dt=0.01, + sp_tol=1e-4, ) model.k_max = 4 # model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-4, n=1, detune=.5, δ=[.1,.1]) @@ -240,7 +242,7 @@ ts = np.linspace(0,50,1000) fig, ax = fs.plot_complex(ts, model.bcf(0)(ts)) fs.plot_complex(ts, model.thermal_correlations(0)(ts)) -proc = model.thermal_process(0) +proc = model.thermal_process(1) import hops z=hops.core.utility.uni_to_gauss(np.random.rand(proc.get_num_y() * 2)) proc.new_process(z) @@ -255,7 +257,7 @@ vs = np.linspace(0.1, 10, 100) plt.plot(vs, chi(vs, ω_0)) plt.plot(vs, G_h(vs)) -aux.integrate(model, 10000) +aux.integrate(model, 10) #_, ax = fs.plot_energy_overview(model, markersize=1, ensemble_args=dict(gc_sleep=0.05)) diff --git a/python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org b/python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org index cf3cc52..e6c344a 100644 --- a/python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org +++ b/python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org @@ -22,11 +22,14 @@ Init ray and silence stocproc. #+begin_src jupyter-python import ray ray.shutdown() - ray.init() + ray.init(address="auto") #+end_src #+RESULTS: -: RayContext(dashboard_url='', python_version='3.9.13', ray_version='1.13.0', ray_commit='e4ce38d001dbbe09cd21c497fedd03d692b2be3e', address_info={'node_ip_address': '141.30.17.225', 'raylet_ip_address': '141.30.17.225', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-07-19_10-51-39_214259_16834/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-07-19_10-51-39_214259_16834/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-07-19_10-51-39_214259_16834', 'metrics_export_port': 60152, 'gcs_address': '141.30.17.225:63172', 'address': '141.30.17.225:63172', 'node_id': '252db7cc012cbe0a60910f26a48f27f9f26859d4f26657a03a5bf00a'}) +:RESULTS: +: RayContext(dashboard_url='', python_version='3.9.13', ray_version='1.13.0', ray_commit='e4ce38d001dbbe09cd21c497fedd03d692b2be3e', address_info={'node_ip_address': '141.30.17.225', 'raylet_ip_address': '141.30.17.225', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-07-07_19-11-40_930040_398742/sockets/plasma_store.8', 'raylet_socket_name': '/tmp/ray/session_2022-07-07_19-11-40_930040_398742/sockets/raylet.3', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-07-07_19-11-40_930040_398742', 'metrics_export_port': 42078, 'gcs_address': '141.30.17.16:6379', 'address': '141.30.17.16:6379', 'node_id': 'f0ec7ea2877bae7b0c671e3bc3532313b0601cd2ed54100a4bdbf452'})2022-07-19 16:17:37,436 INFO worker.py:956 -- Connecting to existing Ray cluster at address: 141.30.17.16:6379 +: RayContext(dashboard_url='', python_version='3.9.13', ray_version='1.13.0', ray_commit='e4ce38d001dbbe09cd21c497fedd03d692b2be3e', address_info={'node_ip_address': '141.30.17.225', 'raylet_ip_address': '141.30.17.225', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-07-07_19-11-40_930040_398742/sockets/plasma_store.8', 'raylet_socket_name': '/tmp/ray/session_2022-07-07_19-11-40_930040_398742/sockets/raylet.3', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-07-07_19-11-40_930040_398742', 'metrics_export_port': 42078, 'gcs_address': '141.30.17.16:6379', 'address': '141.30.17.16:6379', 'node_id': 'f0ec7ea2877bae7b0c671e3bc3532313b0601cd2ed54100a4bdbf452'}) +:END: #+begin_src jupyter-python :results none from hops.util.logging_setup import logging_setup @@ -58,6 +61,7 @@ Init ray and silence stocproc. L_mat=qt.sigmax().full(), therm_methods=["fft", "fft"], terms=5, + sp_tol=1e-5, ): # τ_bath = 1 / ω_c τ_mod = 2 * np.pi / Δ @@ -141,7 +145,7 @@ Init ray and silence stocproc. ) ) ,* np.sqrt(δ_init), - ConstantMatrix(np.zeros((2,2))), + ConstantMatrix(np.zeros((2, 2))), Shift( L, initializing_period + τ_off, @@ -165,8 +169,8 @@ Init ray and silence stocproc. k_max=4, bcf_terms=[terms, terms], truncation_scheme="simplex", - driving_process_tolerances=[StocProcTolerances(1e-5, 1e-5)] * 2, - thermal_process_tolerances=[StocProcTolerances(1e-5, 1e-5)] * 2, + driving_process_tolerances=[StocProcTolerances(sp_tol, sp_tol)] * 2, + thermal_process_tolerances=[StocProcTolerances(sp_tol, sp_tol)] * 2, T=[T_c, T_h], L=[L] * 2, H=H, @@ -204,12 +208,12 @@ Init ray and silence stocproc. ε=.05,#.1, ω_c=1, ε_couple=0.7, - n=5, + n=2, detune=.5, ω_0=20, - T_c=10, - T_h=40, - δ=[2.2, 1], + T_c=1e3, + T_h=1e4, + δ=[3.2, 1], γ=.2, switch_cycles=1, therm_initial_state=False, @@ -217,6 +221,7 @@ Init ray and silence stocproc. ε_init=0.1, terms=6, dt=0.01, + sp_tol=1e-4, ) model.k_max = 4 # model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-4, n=1, detune=.5, δ=[.1,.1]) @@ -255,7 +260,7 @@ Init ray and silence stocproc. #+end_src #+RESULTS: -| \(α(0)=1.1\) | \(ω_0=20\) | \(γ=0.5\) | \(Δ=11\) | \(T_c=10\) | \(T_h=40\) | +| \(α(0)=1.6\) | \(ω_0=20\) | \(γ=0.5\) | \(Δ=11\) | \(T_c=1000\) | \(T_h=10000\) | Let's test the assumptions of the paper. @@ -284,18 +289,17 @@ Let's test the assumptions of the paper. #+RESULTS: :RESULTS: -: -[[file:./.ob-jupyter/e87eb72fe5dfd2a77714413ec7469c1c591aced9.svg]] +: +[[file:./.ob-jupyter/e3bbb4efab98d84348844c37a4aa9384d48cdc15.svg]] :END: #+begin_src jupyter-python :tangle nil ωs = np.linspace(0.01, 4 * ω_0, 10000) - def total_sd(n, ω): return model.bcf_scales[n] * ( model.spectral_density(n)(ω) - * (1/(np.expm1(ω / model.T[n]) - 1) + np.heaviside(ω, 0)) + ,* (1/(np.expm1(ω / model.T[n])) + np.heaviside(ω, 0)) ) @@ -338,8 +342,8 @@ Let's test the assumptions of the paper. #+RESULTS: :RESULTS: -[[file:./.ob-jupyter/dc15ee10981f51398bfde454ebb8808a8da10037.svg]] -[[file:./.ob-jupyter/a643f51530ed06a31439d0e6949011fccef4e3e6.svg]] +[[file:./.ob-jupyter/48e681a6140e19141ec80f8e22cfb5c578e97452.svg]] +[[file:./.ob-jupyter/d13566fe82dc8c85a4376af366ddb38f4643c6e4.svg]] :END: #+begin_src jupyter-python @@ -351,12 +355,12 @@ Let's test the assumptions of the paper. #+RESULTS: :RESULTS: |
| | -[[file:./.ob-jupyter/4341159d7693d59e6f76c5305942671c13587b7a.svg]] -[[file:./.ob-jupyter/71cba43a3ab882492f3132c0c621975f4a1cd86d.svg]] +[[file:./.ob-jupyter/533ffeef663f158f1eb3ebccbf211ec3a6ff45b2.svg]] +[[file:./.ob-jupyter/b9dca795dc334e8536ef2fdc9e04e574651ae7f7.svg]] :END: #+begin_src jupyter-python - proc = model.thermal_process(0) + proc = model.thermal_process(1) import hops z=hops.core.utility.uni_to_gauss(np.random.rand(proc.get_num_y() * 2)) proc.new_process(z) @@ -364,10 +368,7 @@ Let's test the assumptions of the paper. #+end_src #+RESULTS: -:RESULTS: -|
| | -[[file:./.ob-jupyter/ed0e8d32f6fd3956cb0c72e0c52e3d695c31f5e2.svg]] -:END: +: bbc5aaaa-6ed1-4dfb-b3af-7e0a7857708b #+begin_src jupyter-python :results none @@ -390,11 +391,94 @@ Let's test the assumptions of the paper. :END: -** TODO Integration +** Integration #+begin_src jupyter-python - aux.integrate(model, 10000) + aux.integrate(model, 10) #+end_src +#+RESULTS: +:RESULTS: +#+begin_example + [INFO hops.core.integration 92605] Choosing the nonlinear integrator. + [INFO hops.core.integration 92605] Using 9 integrators. + [INFO hops.core.integration 92605] Some 10 trajectories have to be integrated. + [INFO hops.core.integration 92605] Using 1820 hierarchy states. + 0% 0/10 [00:00() + ----> 1 aux.integrate(model, 10) + + File ~/src/two_qubit_model/hiro_models/model_auxiliary.py:108, in integrate(model, n, data_path, clear_pd) +  98 # with model_db(data_path) as db: +  99 # if hash in db and "data" db[hash] +  101 supervisor = HOPSSupervisor( +  102 model.hops_config, +  103 n, +  104 data_path=data_path, +  105 data_name=hash, +  106 ) + --> 108 supervisor.integrate(clear_pd) +  110 with supervisor.get_data(True) as data: +  111 with model_db(data_path) as db: + + File ~/src/hops/hops/core/integration.py:1288, in HOPSSupervisor.integrate(self, clear_pd) +  1285 break +  1287 integration.update() + -> 1288 data.new_samples( +  1289 idx=index, +  1290 incomplete=incomplete, +  1291 psi0=psi0, +  1292 aux_states=aux_states, +  1293 stoc_proc=stoc_proc, +  1294 result_type=self.params.HiP.result_type, +  1295 normed=self._normed_average, +  1296 rng_seed=seed, +  1297 ) + + File ~/src/hops/hops/core/signal_delay.py:87, in sig_delay.__exit__(self, exc_type, exc_val, exc_tb) +  84 if len(self.sigh.sigs_caught) > 0 and self.handler is not None: +  85 self.handler(self.sigh.sigs_caught) + ---> 87 self._restore() + + File ~/src/hops/hops/core/signal_delay.py:68, in sig_delay._restore(self) +  66 for i, s in enumerate(self.sigs): +  67 signal.signal(s, self.old_handlers[i]) + ---> 68 self.sigh.emit() + + File ~/src/hops/hops/core/signal_delay.py:42, in SigHandler.emit(self) +  40 for s in self.sigs_caught: +  41 log.info("emit signal '{}'".format(SIG_MAP[s])) + ---> 42 os.kill(os.getpid(), s) + + KeyboardInterrupt: +#+end_example +:END: + #+begin_src jupyter-python #_, ax = fs.plot_energy_overview(model, markersize=1, ensemble_args=dict(gc_sleep=0.05)) @@ -486,6 +570,30 @@ We need the time points where we sample the total energy. #+end_src #+RESULTS: +:RESULTS: +# [goto error] +#+begin_example + --------------------------------------------------------------------------- + RuntimeError Traceback (most recent call last) + Input In [170], in () +  6 ts_end = model.t[ind_end] +  8 # #plt.plot(model.t, model.L[0].operator_norm(model.t)) + ---> 10 with aux.get_data(model) as data: +  11 tot_e = (model.total_energy_from_power(data)) + + File ~/src/two_qubit_model/hiro_models/model_auxiliary.py:146, in get_data(model, data_path, read_only, **kwargs) +  135 return HIData( +  136 path, +  137 hi_key=model.hops_config, +  (...) +  142 **kwargs, +  143 ) +  145 else: + --> 146 raise RuntimeError(f"No data found for model with hash '{hexhash}'.") + + RuntimeError: No data found for model with hash '59011e9f7180d914d68f21cdd8930f5b6c73a161eb03df9d4210b159b9d38ebe'. +#+end_example +:END: #+begin_src jupyter-python diff --git a/python/energy_flow_proper/10_antizeno_engine/poetry.lock b/python/energy_flow_proper/10_antizeno_engine/poetry.lock index 64f4bfd..4e47147 100644 --- a/python/energy_flow_proper/10_antizeno_engine/poetry.lock +++ b/python/energy_flow_proper/10_antizeno_engine/poetry.lock @@ -304,7 +304,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" [[package]] name = "distlib" -version = "0.3.4" +version = "0.3.5" description = "Distribution utilities" category = "main" optional = false @@ -328,7 +328,7 @@ python-versions = "*" [[package]] name = "fastjsonschema" -version = "2.15.3" +version = 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